<<<<<<< Updated upstream <<<<<<< Updated upstream ISBSG Dataset Profiling Report

Overview

Brought to you by YData

Dataset statistics

Number of variables34
Number of observations85
Missing cells318
Missing cells (%)11.0%
Total size in memory22.7 KiB
Average record size in memory273.6 B

Variable types

Numeric16
Text18

Alerts

project_prf_functional_size has 1 (1.2%) missing values Missing
project_prf_normalised_level_1_pdr_ufp has 1 (1.2%) missing values Missing
project_prf_normalised_pdr_ufp has 1 (1.2%) missing values Missing
project_prf_defect_density has 55 (64.7%) missing values Missing
project_prf_speed_of_delivery has 3 (3.5%) missing values Missing
project_prf_manpower_delivery_rate has 30 (35.3%) missing values Missing
project_prf_project_elapsed_time has 2 (2.4%) missing values Missing
project_prf_max_team_size has 27 (31.8%) missing values Missing
tech_tf_tools_used has 31 (36.5%) missing values Missing
people_prf_project_manage_changes has 55 (64.7%) missing values Missing
people_prf_personnel_changes has 55 (64.7%) missing values Missing
project_prf_total_project_cost has 57 (67.1%) missing values Missing
project_prf_defect_density has 18 (21.2%) zeros Zeros
tech_tf_tools_used has 21 (24.7%) zeros Zeros
people_prf_project_manage_changes has 25 (29.4%) zeros Zeros
people_prf_personnel_changes has 21 (24.7%) zeros Zeros

Reproduction

Analysis started2025-05-18 20:34:40.662241
Analysis finished2025-05-18 20:34:41.155997
Duration0.49 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

isbsg_project_id
Real number (ℝ)

Distinct84
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21197.17647
Minimum10279
Maximum32725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:41.390649image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Overview

Brought to you by YData

Dataset statistics

Number of variables36
Number of observations84
Missing cells273
Missing cells (%)9.0%
Total size in memory23.8 KiB
Average record size in memory289.6 B

Variable types

Numeric17
Text19

Alerts

Project (PRF) - Functional Size has 1 (1.2%) missing values Missing
Project (PRF) - Normalised Level 1 PDR (ufp) has 1 (1.2%) missing values Missing
Project (PRF) - Normalised PDR (ufp) has 1 (1.2%) missing values Missing
Project (PRF) - Defect Density has 53 (63.1%) missing values Missing
Project (PRF) - Speed of Delivery has 3 (3.6%) missing values Missing
Project (PRF) - Manpower Delivery Rate has 28 (33.3%) missing values Missing
Project (PRF) - Project Elapsed Time has 2 (2.4%) missing values Missing
Project (PRF) - Max Team Size has 25 (29.8%) missing values Missing
People (PRF) - Project manage changes has 52 (61.9%) missing values Missing
People (PRF) - Personnel changes has 52 (61.9%) missing values Missing
Project (PRF) - Total project cost has 55 (65.5%) missing values Missing
Project (PRF) - Defect Density has 18 (21.4%) zeros Zeros
Tech (TF) - Tools Used has 28 (33.3%) zeros Zeros
People (PRF) - Project manage changes has 27 (32.1%) zeros Zeros
People (PRF) - Personnel changes has 22 (26.2%) zeros Zeros

Reproduction

Analysis started2025-05-15 16:31:50.086424
Analysis finished2025-05-15 16:31:50.656620
Duration0.57 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ISBSG Project ID
Real number (ℝ)

Distinct83
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20951.14286
Minimum10279
Maximum32010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:50.918151image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Overview

Brought to you by YData

Dataset statistics

Number of variables36
Number of observations84
Missing cells273
Missing cells (%)9.0%
Total size in memory23.8 KiB
Average record size in memory289.6 B

Variable types

Numeric17
Text19

Alerts

Project (PRF) - Functional Size has 1 (1.2%) missing values Missing
Project (PRF) - Normalised Level 1 PDR (ufp) has 1 (1.2%) missing values Missing
Project (PRF) - Normalised PDR (ufp) has 1 (1.2%) missing values Missing
Project (PRF) - Defect Density has 53 (63.1%) missing values Missing
Project (PRF) - Speed of Delivery has 3 (3.6%) missing values Missing
Project (PRF) - Manpower Delivery Rate has 28 (33.3%) missing values Missing
Project (PRF) - Project Elapsed Time has 2 (2.4%) missing values Missing
Project (PRF) - Max Team Size has 25 (29.8%) missing values Missing
People (PRF) - Project manage changes has 52 (61.9%) missing values Missing
People (PRF) - Personnel changes has 52 (61.9%) missing values Missing
Project (PRF) - Total project cost has 55 (65.5%) missing values Missing
Project (PRF) - Defect Density has 18 (21.4%) zeros Zeros
Tech (TF) - Tools Used has 28 (33.3%) zeros Zeros
People (PRF) - Project manage changes has 27 (32.1%) zeros Zeros
People (PRF) - Personnel changes has 22 (26.2%) zeros Zeros

Reproduction

Analysis started2025-05-15 16:31:50.086424
Analysis finished2025-05-15 16:31:50.656620
Duration0.57 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ISBSG Project ID
Real number (ℝ)

Distinct83
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20951.14286
Minimum10279
Maximum32010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:50.918151image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10279
5-th percentile11499.4
Q114928
median20465
Q327063
95-th percentile31166
Maximum32725
Range22446
Interquartile range (IQR)12135

Descriptive statistics

Standard deviation6796.647824
Coefficient of variation (CV)0.3206392999
Kurtosis-1.347349809
Mean21197.17647
Median Absolute Deviation (MAD)6120
Skewness0.08873218267
Sum1801760
Variance46194421.65
MonotonicityIncreasing
2025-05-18T21:34:41.657714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10279
5-th percentile11498.8
Q114851.25
median20162
Q326671.25
95-th percentile31036.25
Maximum32010
Range21731
Interquartile range (IQR)11820

Descriptive statistics

Standard deviation6595.852569
Coefficient of variation (CV)0.3148206575
Kurtosis-1.345390458
Mean20951.14286
Median Absolute Deviation (MAD)5832
Skewness0.07811841109
Sum1759896
Variance43505271.11
MonotonicityIncreasing
2025-05-15T17:31:51.158794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31166 2
 
2.4%
10279 1
 
1.2%
25081 1
 
1.2%
26585 1
 
1.2%
26422 1
 
1.2%
26195 1
 
1.2%
26034 1
 
1.2%
25559 1
 
1.2%
25480 1
 
1.2%
25415 1
 
1.2%
Other values (74) 74
87.1%
ValueCountFrequency (%)
10279 1
1.2%
10317 1
1.2%
10572 1
1.2%
11278 1
1.2%
11497 1
1.2%
ValueCountFrequency (%)
32725 1
1.2%
32692 1
1.2%
32296 1
1.2%
31969 1
1.2%
31166 2
2.4%
Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:41.754864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31166 2
 
2.4%
10279 1
 
1.2%
24701 1
 
1.2%
26422 1
 
1.2%
26195 1
 
1.2%
26034 1
 
1.2%
25559 1
 
1.2%
25480 1
 
1.2%
25415 1
 
1.2%
25247 1
 
1.2%
Other values (73) 73
86.9%
ValueCountFrequency (%)
10279 1
1.2%
10317 1
1.2%
10572 1
1.2%
11278 1
1.2%
11497 1
1.2%
ValueCountFrequency (%)
32010 1
1.2%
31969 1
1.2%
31166 2
2.4%
31103 1
1.2%
30658 1
1.2%
Distinct4
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:51.315560image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31166 2
 
2.4%
10279 1
 
1.2%
24701 1
 
1.2%
26422 1
 
1.2%
26195 1
 
1.2%
26034 1
 
1.2%
25559 1
 
1.2%
25480 1
 
1.2%
25415 1
 
1.2%
25247 1
 
1.2%
Other values (73) 73
86.9%
ValueCountFrequency (%)
10279 1
1.2%
10317 1
1.2%
10572 1
1.2%
11278 1
1.2%
11497 1
1.2%
ValueCountFrequency (%)
32010 1
1.2%
31969 1
1.2%
31166 2
2.4%
31103 1
1.2%
30658 1
1.2%
Distinct4
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:51.315560image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters85
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb
2nd rowb
3rd rowb
4th rowa
5th rowb
ValueCountFrequency (%)
b 60
70.6%
a 25
29.4%
2025-05-18T21:34:42.023525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowB
ValueCountFrequency (%)
b 57
67.9%
a 25
29.8%
c 1
 
1.2%
d 1
 
1.2%
2025-05-15T17:31:51.612914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowB
ValueCountFrequency (%)
b 57
67.9%
a 25
29.8%
c 1
 
1.2%
d 1
 
1.2%
2025-05-15T17:31:51.612914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 60
70.6%
a 25
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 60
70.6%
a 25
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 60
70.6%
a 25
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 60
70.6%
a 25
29.4%
Distinct9
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.376471
Minimum2005
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:42.246190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%
Distinct9
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.369048
Minimum2005
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:51.857373image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 57
67.9%
A 25
29.8%
C 1
 
1.2%
D 1
 
1.2%
Distinct9
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.369048
Minimum2005
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:51.857373image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2009
Q12010
median2012
Q32013
95-th percentile2014
Maximum2015
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.132383367
Coefficient of variation (CV)0.001060161237
Kurtosis-0.6246213059
Mean2011.376471
Median Absolute Deviation (MAD)2
Skewness-0.1834733938
Sum170967
Variance4.547058824
MonotonicityNot monotonic
2025-05-18T21:34:42.499183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2008.15
Q12009.75
median2012
Q32013
95-th percentile2014
Maximum2015
Range10
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.166445993
Coefficient of variation (CV)0.001077100195
Kurtosis-0.6793771425
Mean2011.369048
Median Absolute Deviation (MAD)2
Skewness-0.2069757105
Sum168955
Variance4.693488239
MonotonicityNot monotonic
2025-05-15T17:31:52.065306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2008.15
Q12009.75
median2012
Q32013
95-th percentile2014
Maximum2015
Range10
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.166445993
Coefficient of variation (CV)0.001077100195
Kurtosis-0.6793771425
Mean2011.369048
Median Absolute Deviation (MAD)2
Skewness-0.2069757105
Sum168955
Variance4.693488239
MonotonicityNot monotonic
2025-05-15T17:31:52.065306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2010 17
20.0%
2009 16
18.8%
2013 14
16.5%
2014 13
15.3%
2012 13
15.3%
2015 4
 
4.7%
2011 4
 
4.7%
2008 3
 
3.5%
2005 1
 
1.2%
ValueCountFrequency (%)
2005 1
 
1.2%
2008 3
 
3.5%
2009 16
18.8%
2010 17
20.0%
2011 4
 
4.7%
ValueCountFrequency (%)
2015 4
 
4.7%
2014 13
15.3%
2013 14
16.5%
2012 13
15.3%
2011 4
 
4.7%
Distinct12
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:42.690026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2009 16
19.0%
2010 15
17.9%
2013 14
16.7%
2014 13
15.5%
2012 13
15.5%
2015 4
 
4.8%
2011 4
 
4.8%
2008 4
 
4.8%
2005 1
 
1.2%
ValueCountFrequency (%)
2005 1
 
1.2%
2008 4
 
4.8%
2009 16
19.0%
2010 15
17.9%
2011 4
 
4.8%
ValueCountFrequency (%)
2015 4
 
4.8%
2014 13
15.5%
2013 14
16.7%
2012 13
15.5%
2011 4
 
4.8%
Distinct11
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:52.317437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2009 16
19.0%
2010 15
17.9%
2013 14
16.7%
2014 13
15.5%
2012 13
15.5%
2015 4
 
4.8%
2011 4
 
4.8%
2008 4
 
4.8%
2005 1
 
1.2%
ValueCountFrequency (%)
2005 1
 
1.2%
2008 4
 
4.8%
2009 16
19.0%
2010 15
17.9%
2011 4
 
4.8%
ValueCountFrequency (%)
2015 4
 
4.8%
2014 13
15.5%
2013 14
16.7%
2012 13
15.5%
2011 4
 
4.8%
Distinct11
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:52.317437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length23
Median length21
Mean length10.94117647
Min length7

Characters and Unicode

Total characters930
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)5.9%

Sample

1st rowbanking
2nd rowgovernment
3rd rowgovernment
4th rowservice industry
5th rowbanking
ValueCountFrequency (%)
banking 25
21.6%
government 20
17.2%
service 14
12.1%
industry 14
12.1%
education 13
11.2%
7
 
6.0%
care 3
 
2.6%
computers 3
 
2.6%
electronics 3
 
2.6%
health 3
 
2.6%
Other values (8) 11
9.5%
2025-05-18T21:34:43.231076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length23
Median length21
Mean length11.03571429
Min length7

Characters and Unicode

Total characters927
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)4.8%

Sample

1st rowBanking
2nd rowGovernment
3rd rowGovernment
4th rowService Industry
5th rowBanking
ValueCountFrequency (%)
banking 24
20.9%
government 20
17.4%
service 14
12.2%
industry 14
12.2%
education 13
11.3%
7
 
6.1%
care 3
 
2.6%
computers 3
 
2.6%
electronics 3
 
2.6%
health 3
 
2.6%
Other values (7) 11
9.6%
2025-05-15T17:31:52.851695image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length23
Median length21
Mean length11.03571429
Min length7

Characters and Unicode

Total characters927
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)4.8%

Sample

1st rowBanking
2nd rowGovernment
3rd rowGovernment
4th rowService Industry
5th rowBanking
ValueCountFrequency (%)
banking 24
20.9%
government 20
17.4%
service 14
12.2%
industry 14
12.2%
education 13
11.3%
7
 
6.1%
care 3
 
2.6%
computers 3
 
2.6%
electronics 3
 
2.6%
health 3
 
2.6%
Other values (7) 11
9.6%
2025-05-15T17:31:52.851695image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 129
13.9%
e 103
 
11.1%
i 84
 
9.0%
t 62
 
6.7%
r 60
 
6.5%
a 56
 
6.0%
g 48
 
5.2%
c 47
 
5.1%
o 42
 
4.5%
s 38
 
4.1%
Other values (15) 261
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 129
13.9%
e 103
 
11.1%
i 84
 
9.0%
t 62
 
6.7%
r 60
 
6.5%
a 56
 
6.0%
g 48
 
5.2%
c 47
 
5.1%
o 42
 
4.5%
s 38
 
4.1%
Other values (15) 261
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 129
13.9%
e 103
 
11.1%
i 84
 
9.0%
t 62
 
6.7%
r 60
 
6.5%
a 56
 
6.0%
g 48
 
5.2%
c 47
 
5.1%
o 42
 
4.5%
s 38
 
4.1%
Other values (15) 261
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 129
13.9%
e 103
 
11.1%
i 84
 
9.0%
t 62
 
6.7%
r 60
 
6.5%
a 56
 
6.0%
g 48
 
5.2%
c 47
 
5.1%
o 42
 
4.5%
s 38
 
4.1%
Other values (15) 261
28.1%
Distinct25
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:43.420963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 129
13.9%
e 86
 
9.3%
i 67
 
7.2%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.1%
o 42
 
4.5%
c 41
 
4.4%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 313
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 129
13.9%
e 86
 
9.3%
i 67
 
7.2%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.1%
o 42
 
4.5%
c 41
 
4.4%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 313
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 129
13.9%
e 86
 
9.3%
i 67
 
7.2%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.1%
o 42
 
4.5%
c 41
 
4.4%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 313
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 129
13.9%
e 86
 
9.3%
i 67
 
7.2%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.1%
o 42
 
4.5%
c 41
 
4.4%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 313
33.8%
Distinct25
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:53.019512image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 129
13.9%
e 86
 
9.3%
i 67
 
7.2%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.1%
o 42
 
4.5%
c 41
 
4.4%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 313
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 129
13.9%
e 86
 
9.3%
i 67
 
7.2%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.1%
o 42
 
4.5%
c 41
 
4.4%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 313
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 129
13.9%
e 86
 
9.3%
i 67
 
7.2%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.1%
o 42
 
4.5%
c 41
 
4.4%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 313
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 129
13.9%
e 86
 
9.3%
i 67
 
7.2%
t 61
 
6.6%
r 60
 
6.5%
a 57
 
6.1%
o 42
 
4.5%
c 41
 
4.4%
u 37
 
4.0%
v 34
 
3.7%
Other values (23) 313
33.8%
Distinct25
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:53.019512image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length130
Median length58
Mean length53.85882353
Min length6

Characters and Unicode

Total characters4578
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)15.3%

Sample

1st rowbanking; communications; education institution; government; medical and health care; transport & storage; wholesale & retail trade
2nd rowgovernment
3rd rowgovernment
4th rowcommunity services
5th rowbanking; communications; education institution; government; medical and health care; transport & storage; wholesale & retail trade
ValueCountFrequency (%)
62
 
11.1%
government 45
 
8.1%
education 37
 
6.6%
institution 37
 
6.6%
transport 27
 
4.8%
storage 27
 
4.8%
medical 26
 
4.7%
and 26
 
4.7%
health 26
 
4.7%
care 26
 
4.7%
Other values (43) 220
39.4%
2025-05-18T21:34:43.995233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length254
Median length91
Mean length54.94047619
Min length8

Characters and Unicode

Total characters4615
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)15.5%

Sample

1st rowGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;
2nd rowGovernment;
3rd rowGovernment;
4th rowCommunity Services;
5th rowGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;
ValueCountFrequency (%)
64
16.2%
and 26
 
6.6%
health 26
 
6.6%
retail 24
 
6.1%
trade;transport 24
 
6.1%
storage;communications;medical 24
 
6.1%
government;education 23
 
5.8%
institution;wholesale 23
 
5.8%
care;banking 23
 
5.8%
government 19
 
4.8%
Other values (52) 120
30.3%
2025-05-15T17:31:53.399801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length254
Median length91
Mean length54.94047619
Min length8

Characters and Unicode

Total characters4615
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)15.5%

Sample

1st rowGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;
2nd rowGovernment;
3rd rowGovernment;
4th rowCommunity Services;
5th rowGovernment;Education Institution;Wholesale & Retail Trade;Transport & Storage;Communications;Medical and Health Care;Banking;
ValueCountFrequency (%)
64
16.2%
and 26
 
6.6%
health 26
 
6.6%
retail 24
 
6.1%
trade;transport 24
 
6.1%
storage;communications;medical 24
 
6.1%
government;education 23
 
5.8%
institution;wholesale 23
 
5.8%
care;banking 23
 
5.8%
government 19
 
4.8%
Other values (52) 120
30.3%
2025-05-15T17:31:53.399801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
474
 
10.4%
t 451
 
9.9%
e 434
 
9.5%
n 390
 
8.5%
a 359
 
7.8%
i 355
 
7.8%
o 275
 
6.0%
r 254
 
5.5%
s 191
 
4.2%
c 191
 
4.2%
Other values (19) 1204
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
474
 
10.4%
t 451
 
9.9%
e 434
 
9.5%
n 390
 
8.5%
a 359
 
7.8%
i 355
 
7.8%
o 275
 
6.0%
r 254
 
5.5%
s 191
 
4.2%
c 191
 
4.2%
Other values (19) 1204
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
474
 
10.4%
t 451
 
9.9%
e 434
 
9.5%
n 390
 
8.5%
a 359
 
7.8%
i 355
 
7.8%
o 275
 
6.0%
r 254
 
5.5%
s 191
 
4.2%
c 191
 
4.2%
Other values (19) 1204
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
474
 
10.4%
t 451
 
9.9%
e 434
 
9.5%
n 390
 
8.5%
a 359
 
7.8%
i 355
 
7.8%
o 275
 
6.0%
r 254
 
5.5%
s 191
 
4.2%
c 191
 
4.2%
Other values (19) 1204
26.3%
Distinct5
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:44.266325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%
Distinct5
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:53.538858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 401
 
8.7%
n 399
 
8.6%
e 376
 
8.1%
a 358
 
7.8%
i 319
 
6.9%
312
 
6.8%
o 282
 
6.1%
; 260
 
5.6%
r 238
 
5.2%
s 162
 
3.5%
Other values (35) 1508
32.7%
Distinct5
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:53.538858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length36
Median length20
Mean length19.51764706
Min length7

Characters and Unicode

Total characters1659
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowbusiness application
2nd rowbusiness application
3rd rowbusiness application
4th rowbusiness application
5th rowbusiness application
ValueCountFrequency (%)
application 78
47.3%
business 75
45.5%
missing 5
 
3.0%
real-time 2
 
1.2%
infrastructure 2
 
1.2%
software 2
 
1.2%
mathematically-intensive 1
 
0.6%
2025-05-18T21:34:44.705349image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length36
Median length20
Mean length19.51190476
Min length7

Characters and Unicode

Total characters1639
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowBusiness Application
2nd rowBusiness Application
3rd rowBusiness Application
4th rowBusiness Application
5th rowBusiness Application
ValueCountFrequency (%)
application 77
47.2%
business 74
45.4%
missing 5
 
3.1%
real-time 2
 
1.2%
infrastructure 2
 
1.2%
software 2
 
1.2%
mathematically-intensive 1
 
0.6%
2025-05-15T17:31:53.889874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length36
Median length20
Mean length19.51190476
Min length7

Characters and Unicode

Total characters1639
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowBusiness Application
2nd rowBusiness Application
3rd rowBusiness Application
4th rowBusiness Application
5th rowBusiness Application
ValueCountFrequency (%)
application 77
47.2%
business 74
45.4%
missing 5
 
3.1%
real-time 2
 
1.2%
infrastructure 2
 
1.2%
software 2
 
1.2%
mathematically-intensive 1
 
0.6%
2025-05-15T17:31:53.889874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 248
14.9%
s 240
14.5%
a 165
9.9%
n 162
9.8%
p 156
9.4%
t 89
 
5.4%
e 86
 
5.2%
l 82
 
4.9%
c 81
 
4.9%
o 80
 
4.8%
Other values (13) 270
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1659
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 248
14.9%
s 240
14.5%
a 165
9.9%
n 162
9.8%
p 156
9.4%
t 89
 
5.4%
e 86
 
5.2%
l 82
 
4.9%
c 81
 
4.9%
o 80
 
4.8%
Other values (13) 270
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1659
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 248
14.9%
s 240
14.5%
a 165
9.9%
n 162
9.8%
p 156
9.4%
t 89
 
5.4%
e 86
 
5.2%
l 82
 
4.9%
c 81
 
4.9%
o 80
 
4.8%
Other values (13) 270
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1659
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 248
14.9%
s 240
14.5%
a 165
9.9%
n 162
9.8%
p 156
9.4%
t 89
 
5.4%
e 86
 
5.2%
l 82
 
4.9%
c 81
 
4.9%
o 80
 
4.8%
Other values (13) 270
16.3%
Distinct32
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:45.013631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 242
14.8%
s 235
14.3%
n 160
9.8%
p 154
9.4%
a 86
 
5.2%
t 86
 
5.2%
e 85
 
5.2%
l 81
 
4.9%
c 80
 
4.9%
79
 
4.8%
Other values (18) 351
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1639
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 242
14.8%
s 235
14.3%
n 160
9.8%
p 154
9.4%
a 86
 
5.2%
t 86
 
5.2%
e 85
 
5.2%
l 81
 
4.9%
c 80
 
4.9%
79
 
4.8%
Other values (18) 351
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1639
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 242
14.8%
s 235
14.3%
n 160
9.8%
p 154
9.4%
a 86
 
5.2%
t 86
 
5.2%
e 85
 
5.2%
l 81
 
4.9%
c 80
 
4.9%
79
 
4.8%
Other values (18) 351
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1639
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 242
14.8%
s 235
14.3%
n 160
9.8%
p 154
9.4%
a 86
 
5.2%
t 86
 
5.2%
e 85
 
5.2%
l 81
 
4.9%
c 80
 
4.9%
79
 
4.8%
Other values (18) 351
21.4%
Distinct32
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:54.174896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 242
14.8%
s 235
14.3%
n 160
9.8%
p 154
9.4%
a 86
 
5.2%
t 86
 
5.2%
e 85
 
5.2%
l 81
 
4.9%
c 80
 
4.9%
79
 
4.8%
Other values (18) 351
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1639
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 242
14.8%
s 235
14.3%
n 160
9.8%
p 154
9.4%
a 86
 
5.2%
t 86
 
5.2%
e 85
 
5.2%
l 81
 
4.9%
c 80
 
4.9%
79
 
4.8%
Other values (18) 351
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1639
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 242
14.8%
s 235
14.3%
n 160
9.8%
p 154
9.4%
a 86
 
5.2%
t 86
 
5.2%
e 85
 
5.2%
l 81
 
4.9%
c 80
 
4.9%
79
 
4.8%
Other values (18) 351
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1639
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 242
14.8%
s 235
14.3%
n 160
9.8%
p 154
9.4%
a 86
 
5.2%
t 86
 
5.2%
e 85
 
5.2%
l 81
 
4.9%
c 80
 
4.9%
79
 
4.8%
Other values (18) 351
21.4%
Distinct32
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:54.174896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length216
Median length100
Mean length36.23529412
Min length3

Characters and Unicode

Total characters3080
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)24.7%

Sample

1st rowsurveillance and security
2nd rowbusiness application
3rd rowbusiness application
4th rowcomplex process control; workflow support & management
5th rowsurveillance and security
ValueCountFrequency (%)
management 32
 
8.9%
and 29
 
8.1%
security 25
 
6.9%
surveillance 24
 
6.7%
system 16
 
4.4%
application 15
 
4.2%
business 14
 
3.9%
12
 
3.3%
website 11
 
3.1%
dynamic 11
 
3.1%
Other values (69) 171
47.5%
2025-05-18T21:34:45.547915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length255
Median length101
Mean length39.5952381
Min length4

Characters and Unicode

Total characters3326
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)25.0%

Sample

1st rowSurveillance and security;
2nd rowBusiness Application;
3rd rowBusiness Application;
4th rowWorkflow support & management;Complex process control;
5th rowSurveillance and security;
ValueCountFrequency (%)
and 29
 
8.6%
security 24
 
7.1%
surveillance 23
 
6.8%
management 22
 
6.5%
application 15
 
4.5%
business 14
 
4.2%
12
 
3.6%
system;dynamic 11
 
3.3%
website 11
 
3.3%
or 10
 
3.0%
Other values (80) 166
49.3%
2025-05-15T17:31:54.707978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length255
Median length101
Mean length39.5952381
Min length4

Characters and Unicode

Total characters3326
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)25.0%

Sample

1st rowSurveillance and security;
2nd rowBusiness Application;
3rd rowBusiness Application;
4th rowWorkflow support & management;Complex process control;
5th rowSurveillance and security;
ValueCountFrequency (%)
and 29
 
8.6%
security 24
 
7.1%
surveillance 23
 
6.8%
management 22
 
6.5%
application 15
 
4.5%
business 14
 
4.2%
12
 
3.6%
system;dynamic 11
 
3.3%
website 11
 
3.3%
or 10
 
3.0%
Other values (80) 166
49.3%
2025-05-15T17:31:54.707978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 310
 
10.1%
n 280
 
9.1%
275
 
8.9%
a 252
 
8.2%
s 223
 
7.2%
i 217
 
7.0%
t 216
 
7.0%
c 161
 
5.2%
r 156
 
5.1%
o 154
 
5.0%
Other values (24) 836
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 310
 
10.1%
n 280
 
9.1%
275
 
8.9%
a 252
 
8.2%
s 223
 
7.2%
i 217
 
7.0%
t 216
 
7.0%
c 161
 
5.2%
r 156
 
5.1%
o 154
 
5.0%
Other values (24) 836
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 310
 
10.1%
n 280
 
9.1%
275
 
8.9%
a 252
 
8.2%
s 223
 
7.2%
i 217
 
7.0%
t 216
 
7.0%
c 161
 
5.2%
r 156
 
5.1%
o 154
 
5.0%
Other values (24) 836
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 310
 
10.1%
n 280
 
9.1%
275
 
8.9%
a 252
 
8.2%
s 223
 
7.2%
i 217
 
7.0%
t 216
 
7.0%
c 161
 
5.2%
r 156
 
5.1%
o 154
 
5.0%
Other values (24) 836
27.1%
Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:45.795681image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%
Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:54.866197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 322
 
9.7%
n 301
 
9.0%
253
 
7.6%
a 243
 
7.3%
t 227
 
6.8%
i 225
 
6.8%
s 198
 
6.0%
r 166
 
5.0%
o 166
 
5.0%
c 144
 
4.3%
Other values (41) 1081
32.5%
Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:54.866197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length15
Median length11
Mean length12.64705882
Min length11

Characters and Unicode

Total characters1075
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowenhancement
2nd rowenhancement
3rd rowenhancement
4th rowenhancement
5th rowenhancement
ValueCountFrequency (%)
enhancement 49
41.9%
new 32
27.4%
development 32
27.4%
re-development 4
 
3.4%
2025-05-18T21:34:46.343156image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length15
Median length11
Mean length12.66666667
Min length11

Characters and Unicode

Total characters1064
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnhancement
2nd rowEnhancement
3rd rowEnhancement
4th rowEnhancement
5th rowEnhancement
ValueCountFrequency (%)
enhancement 48
41.4%
new 32
27.6%
development 32
27.6%
re-development 4
 
3.4%
2025-05-15T17:31:55.285471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length15
Median length11
Mean length12.66666667
Min length11

Characters and Unicode

Total characters1064
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnhancement
2nd rowEnhancement
3rd rowEnhancement
4th rowEnhancement
5th rowEnhancement
ValueCountFrequency (%)
enhancement 48
41.4%
new 32
27.6%
development 32
27.6%
re-development 4
 
3.4%
2025-05-15T17:31:55.285471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 291
27.1%
n 215
20.0%
m 85
 
7.9%
t 85
 
7.9%
h 49
 
4.6%
a 49
 
4.6%
c 49
 
4.6%
d 36
 
3.3%
v 36
 
3.3%
l 36
 
3.3%
Other values (6) 144
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1075
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 291
27.1%
n 215
20.0%
m 85
 
7.9%
t 85
 
7.9%
h 49
 
4.6%
a 49
 
4.6%
c 49
 
4.6%
d 36
 
3.3%
v 36
 
3.3%
l 36
 
3.3%
Other values (6) 144
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1075
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 291
27.1%
n 215
20.0%
m 85
 
7.9%
t 85
 
7.9%
h 49
 
4.6%
a 49
 
4.6%
c 49
 
4.6%
d 36
 
3.3%
v 36
 
3.3%
l 36
 
3.3%
Other values (6) 144
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1075
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 291
27.1%
n 215
20.0%
m 85
 
7.9%
t 85
 
7.9%
h 49
 
4.6%
a 49
 
4.6%
c 49
 
4.6%
d 36
 
3.3%
v 36
 
3.3%
l 36
 
3.3%
Other values (6) 144
13.4%
Distinct5
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:46.489662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%
Distinct5
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:55.415838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 240
22.6%
n 180
16.9%
m 84
 
7.9%
t 84
 
7.9%
E 48
 
4.5%
h 48
 
4.5%
a 48
 
4.5%
c 48
 
4.5%
v 36
 
3.4%
p 36
 
3.4%
Other values (9) 212
19.9%
Distinct5
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:55.415838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length11
Median length2
Mean length4.470588235
Min length2

Characters and Unicode

Total characters380
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowpc
2nd rowMissing
3rd rowMissing
4th rowmulti
5th rowpc
ValueCountFrequency (%)
pc 48
56.5%
missing 15
 
17.6%
proprietary 12
 
14.1%
multi 9
 
10.6%
mr 1
 
1.2%
2025-05-18T21:34:46.868797image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length11
Median length2
Mean length4.285714286
Min length2

Characters and Unicode

Total characters360
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowPC
2nd rowMissing
3rd rowMissing
4th rowMulti
5th rowPC
ValueCountFrequency (%)
pc 49
58.3%
missing 15
 
17.9%
proprietary 10
 
11.9%
multi 9
 
10.7%
mr 1
 
1.2%
2025-05-15T17:31:55.754339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length11
Median length2
Mean length4.285714286
Min length2

Characters and Unicode

Total characters360
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowPC
2nd rowMissing
3rd rowMissing
4th rowMulti
5th rowPC
ValueCountFrequency (%)
pc 49
58.3%
missing 15
 
17.9%
proprietary 10
 
11.9%
multi 9
 
10.7%
mr 1
 
1.2%
2025-05-15T17:31:55.754339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 72
18.9%
i 51
13.4%
c 48
12.6%
r 37
9.7%
s 30
7.9%
t 21
 
5.5%
M 15
 
3.9%
n 15
 
3.9%
g 15
 
3.9%
o 12
 
3.2%
Other values (6) 64
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 72
18.9%
i 51
13.4%
c 48
12.6%
r 37
9.7%
s 30
7.9%
t 21
 
5.5%
M 15
 
3.9%
n 15
 
3.9%
g 15
 
3.9%
o 12
 
3.2%
Other values (6) 64
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 72
18.9%
i 51
13.4%
c 48
12.6%
r 37
9.7%
s 30
7.9%
t 21
 
5.5%
M 15
 
3.9%
n 15
 
3.9%
g 15
 
3.9%
o 12
 
3.2%
Other values (6) 64
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 72
18.9%
i 51
13.4%
c 48
12.6%
r 37
9.7%
s 30
7.9%
t 21
 
5.5%
M 15
 
3.9%
n 15
 
3.9%
g 15
 
3.9%
o 12
 
3.2%
Other values (6) 64
16.8%
Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:47.030419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 59
16.4%
i 49
13.6%
C 49
13.6%
s 30
8.3%
r 30
8.3%
M 25
6.9%
t 19
 
5.3%
n 15
 
4.2%
g 15
 
4.2%
y 10
 
2.8%
Other values (7) 59
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 59
16.4%
i 49
13.6%
C 49
13.6%
s 30
8.3%
r 30
8.3%
M 25
6.9%
t 19
 
5.3%
n 15
 
4.2%
g 15
 
4.2%
y 10
 
2.8%
Other values (7) 59
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 59
16.4%
i 49
13.6%
C 49
13.6%
s 30
8.3%
r 30
8.3%
M 25
6.9%
t 19
 
5.3%
n 15
 
4.2%
g 15
 
4.2%
y 10
 
2.8%
Other values (7) 59
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 59
16.4%
i 49
13.6%
C 49
13.6%
s 30
8.3%
r 30
8.3%
M 25
6.9%
t 19
 
5.3%
n 15
 
4.2%
g 15
 
4.2%
y 10
 
2.8%
Other values (7) 59
16.4%
Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:55.928379image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 59
16.4%
i 49
13.6%
C 49
13.6%
s 30
8.3%
r 30
8.3%
M 25
6.9%
t 19
 
5.3%
n 15
 
4.2%
g 15
 
4.2%
y 10
 
2.8%
Other values (7) 59
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 59
16.4%
i 49
13.6%
C 49
13.6%
s 30
8.3%
r 30
8.3%
M 25
6.9%
t 19
 
5.3%
n 15
 
4.2%
g 15
 
4.2%
y 10
 
2.8%
Other values (7) 59
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 59
16.4%
i 49
13.6%
C 49
13.6%
s 30
8.3%
r 30
8.3%
M 25
6.9%
t 19
 
5.3%
n 15
 
4.2%
g 15
 
4.2%
y 10
 
2.8%
Other values (7) 59
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 59
16.4%
i 49
13.6%
C 49
13.6%
s 30
8.3%
r 30
8.3%
M 25
6.9%
t 19
 
5.3%
n 15
 
4.2%
g 15
 
4.2%
y 10
 
2.8%
Other values (7) 59
16.4%
Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:55.928379image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters255
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3GL
2nd row4GL
3rd row4GL
4th row3GL
5th row3GL
ValueCountFrequency (%)
3gl 55
64.7%
4gl 18
 
21.2%
5gl 12
 
14.1%
2025-05-18T21:34:47.371799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3GL
2nd row4GL
3rd row4GL
4th row3GL
5th row3GL
ValueCountFrequency (%)
3gl 56
66.7%
4gl 18
 
21.4%
5gl 10
 
11.9%
2025-05-15T17:31:56.238123image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3GL
2nd row4GL
3rd row4GL
4th row3GL
5th row3GL
ValueCountFrequency (%)
3gl 56
66.7%
4gl 18
 
21.4%
5gl 10
 
11.9%
2025-05-15T17:31:56.238123image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 85
33.3%
L 85
33.3%
3 55
21.6%
4 18
 
7.1%
5 12
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 85
33.3%
L 85
33.3%
3 55
21.6%
4 18
 
7.1%
5 12
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 85
33.3%
L 85
33.3%
3 55
21.6%
4 18
 
7.1%
5 12
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 85
33.3%
L 85
33.3%
3 55
21.6%
4 18
 
7.1%
5 12
 
4.7%
Distinct9
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:47.559538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%
Distinct9
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:56.380259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 84
33.3%
L 84
33.3%
3 56
22.2%
4 18
 
7.1%
5 10
 
4.0%
Distinct9
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:56.380259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length26
Median length10
Mean length6.635294118
Min length2

Characters and Unicode

Total characters564
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st rowc#
2nd row.net
3rd roworacle
4th rowjavascript
5th rowc#
ValueCountFrequency (%)
c 36
33.0%
java 16
14.7%
proprietary 12
 
11.0%
agile 12
 
11.0%
platform 12
 
11.0%
oracle 9
 
8.3%
net 8
 
7.3%
javascript 2
 
1.8%
abap 1
 
0.9%
pl/i 1
 
0.9%
2025-05-18T21:34:47.978174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length26
Median length10
Mean length6.119047619
Min length2

Characters and Unicode

Total characters514
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st rowC#
2nd row.Net
3rd rowOracle
4th rowJavaScript
5th rowC#
ValueCountFrequency (%)
c 37
35.6%
java 16
15.4%
proprietary 10
 
9.6%
agile 10
 
9.6%
platform 10
 
9.6%
oracle 9
 
8.7%
net 8
 
7.7%
javascript 2
 
1.9%
abap 1
 
1.0%
pl/i 1
 
1.0%
2025-05-15T17:31:56.703065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length26
Median length10
Mean length6.119047619
Min length2

Characters and Unicode

Total characters514
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st rowC#
2nd row.Net
3rd rowOracle
4th rowJavaScript
5th rowC#
ValueCountFrequency (%)
c 37
35.6%
java 16
15.4%
proprietary 10
 
9.6%
agile 10
 
9.6%
platform 10
 
9.6%
oracle 9
 
8.7%
net 8
 
7.7%
javascript 2
 
1.9%
abap 1
 
1.0%
pl/i 1
 
1.0%
2025-05-15T17:31:56.703065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 83
14.7%
r 59
10.5%
c 47
 
8.3%
e 41
 
7.3%
p 40
 
7.1%
t 34
 
6.0%
# 34
 
6.0%
l 34
 
6.0%
o 33
 
5.9%
i 27
 
4.8%
Other values (13) 132
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 83
14.7%
r 59
10.5%
c 47
 
8.3%
e 41
 
7.3%
p 40
 
7.1%
t 34
 
6.0%
# 34
 
6.0%
l 34
 
6.0%
o 33
 
5.9%
i 27
 
4.8%
Other values (13) 132
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 83
14.7%
r 59
10.5%
c 47
 
8.3%
e 41
 
7.3%
p 40
 
7.1%
t 34
 
6.0%
# 34
 
6.0%
l 34
 
6.0%
o 33
 
5.9%
i 27
 
4.8%
Other values (13) 132
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 83
14.7%
r 59
10.5%
c 47
 
8.3%
e 41
 
7.3%
p 40
 
7.1%
t 34
 
6.0%
# 34
 
6.0%
l 34
 
6.0%
o 33
 
5.9%
i 27
 
4.8%
Other values (13) 132
23.4%

project_prf_functional_size
Real number (ℝ)

Missing 

Distinct75
Distinct (%)89.3%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean188.4642857
Minimum2
Maximum2003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:48.207735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 514
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 514
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 514
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Project (PRF) - Functional Size
Real number (ℝ)

Missing 

Distinct75
Distinct (%)90.4%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean240.3493976
Minimum2
Maximum5393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:56.934598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 514
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 514
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 514
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 65
 
12.6%
r 51
 
9.9%
C 37
 
7.2%
e 37
 
7.2%
# 35
 
6.8%
t 30
 
5.8%
l 29
 
5.6%
P 22
 
4.3%
i 22
 
4.3%
20
 
3.9%
Other values (19) 166
32.3%

Project (PRF) - Functional Size
Real number (ℝ)

Missing 

Distinct75
Distinct (%)90.4%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean240.3493976
Minimum2
Maximum5393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:56.934598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8.15
Q123.75
median83.5
Q3170.25
95-th percentile740.05
Maximum2003
Range2001
Interquartile range (IQR)146.5

Descriptive statistics

Standard deviation316.1823268
Coefficient of variation (CV)1.677677686
Kurtosis14.74613788
Mean188.4642857
Median Absolute Deviation (MAD)65.5
Skewness3.474101537
Sum15831
Variance99971.26377
MonotonicityNot monotonic
2025-05-18T21:34:48.489107image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8.1
Q124.5
median85
Q3172.5
95-th percentile742.7
Maximum5393
Range5391
Interquartile range (IQR)148

Descriptive statistics

Standard deviation643.2239574
Coefficient of variation (CV)2.676203743
Kurtosis51.73593346
Mean240.3493976
Median Absolute Deviation (MAD)67
Skewness6.723330608
Sum19949
Variance413737.0594
MonotonicityNot monotonic
2025-05-15T17:31:57.173465image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 2
 
2.4%
51 2
 
2.4%
15 2
 
2.4%
16 2
 
2.4%
45 2
 
2.4%
18 2
 
2.4%
138 2
 
2.4%
13 2
 
2.4%
243 2
 
2.4%
209 1
 
1.2%
Other values (65) 65
76.5%
ValueCountFrequency (%)
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
8 1
1.2%
ValueCountFrequency (%)
2003 1
1.2%
1371 1
1.2%
995 1
1.2%
912 1
1.2%
748 1
1.2%
Distinct7
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:48.711162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
243 2
 
2.4%
138 2
 
2.4%
72 2
 
2.4%
45 2
 
2.4%
13 2
 
2.4%
18 2
 
2.4%
51 2
 
2.4%
15 2
 
2.4%
155 1
 
1.2%
113 1
 
1.2%
Other values (65) 65
77.4%
ValueCountFrequency (%)
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
8 1
1.2%
ValueCountFrequency (%)
5393 1
1.2%
2003 1
1.2%
1107 1
1.2%
912 1
1.2%
748 1
1.2%
Distinct8
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:31:57.329612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length2
Mean length1.833333333
Min length1

Characters and Unicode

Total characters154
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st rowXS
2nd rowXXS
3rd rowS
4th rowS
5th rowS
ValueCountFrequency (%)
m1 24
28.6%
s 23
27.4%
xs 17
20.2%
m2 10
11.9%
xxs 6
 
7.1%
l 2
 
2.4%
missing 1
 
1.2%
xl 1
 
1.2%
2025-05-15T17:31:57.729121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length2
Mean length1.835294118
Min length1

Characters and Unicode

Total characters156
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowxs
2nd rowxxs
3rd rows
4th rows
5th rows
ValueCountFrequency (%)
m1 24
28.2%
s 23
27.1%
xs 18
21.2%
m2 11
12.9%
xxs 6
 
7.1%
l 2
 
2.4%
missing 1
 
1.2%
2025-05-18T21:34:49.055198image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 49
31.4%
m 35
22.4%
x 30
19.2%
1 24
15.4%
2 11
 
7.1%
l 2
 
1.3%
i 2
 
1.3%
M 1
 
0.6%
n 1
 
0.6%
g 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 49
31.4%
m 35
22.4%
x 30
19.2%
1 24
15.4%
2 11
 
7.1%
l 2
 
1.3%
i 2
 
1.3%
M 1
 
0.6%
n 1
 
0.6%
g 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 49
31.4%
m 35
22.4%
x 30
19.2%
1 24
15.4%
2 11
 
7.1%
l 2
 
1.3%
i 2
 
1.3%
M 1
 
0.6%
n 1
 
0.6%
g 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 49
31.4%
m 35
22.4%
x 30
19.2%
1 24
15.4%
2 11
 
7.1%
l 2
 
1.3%
i 2
 
1.3%
M 1
 
0.6%
n 1
 
0.6%
g 1
 
0.6%
Distinct78
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2590.352941
Minimum6
Maximum60047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:49.263342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 46
29.9%
M 35
22.7%
X 30
19.5%
1 24
15.6%
2 10
 
6.5%
L 3
 
1.9%
i 2
 
1.3%
s 2
 
1.3%
n 1
 
0.6%
g 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 46
29.9%
M 35
22.7%
X 30
19.5%
1 24
15.6%
2 10
 
6.5%
L 3
 
1.9%
i 2
 
1.3%
s 2
 
1.3%
n 1
 
0.6%
g 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 46
29.9%
M 35
22.7%
X 30
19.5%
1 24
15.6%
2 10
 
6.5%
L 3
 
1.9%
i 2
 
1.3%
s 2
 
1.3%
n 1
 
0.6%
g 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 46
29.9%
M 35
22.7%
X 30
19.5%
1 24
15.6%
2 10
 
6.5%
L 3
 
1.9%
i 2
 
1.3%
s 2
 
1.3%
n 1
 
0.6%
g 1
 
0.6%
Distinct75
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2935.77381
Minimum6
Maximum52743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:57.994563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length2
Mean length1.833333333
Min length1

Characters and Unicode

Total characters154
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st rowXS
2nd rowXXS
3rd rowS
4th rowS
5th rowS
ValueCountFrequency (%)
m1 24
28.6%
s 23
27.4%
xs 17
20.2%
m2 10
11.9%
xxs 6
 
7.1%
l 2
 
2.4%
missing 1
 
1.2%
xl 1
 
1.2%
2025-05-15T17:31:57.729121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 46
29.9%
M 35
22.7%
X 30
19.5%
1 24
15.6%
2 10
 
6.5%
L 3
 
1.9%
i 2
 
1.3%
s 2
 
1.3%
n 1
 
0.6%
g 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 46
29.9%
M 35
22.7%
X 30
19.5%
1 24
15.6%
2 10
 
6.5%
L 3
 
1.9%
i 2
 
1.3%
s 2
 
1.3%
n 1
 
0.6%
g 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 46
29.9%
M 35
22.7%
X 30
19.5%
1 24
15.6%
2 10
 
6.5%
L 3
 
1.9%
i 2
 
1.3%
s 2
 
1.3%
n 1
 
0.6%
g 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 46
29.9%
M 35
22.7%
X 30
19.5%
1 24
15.6%
2 10
 
6.5%
L 3
 
1.9%
i 2
 
1.3%
s 2
 
1.3%
n 1
 
0.6%
g 1
 
0.6%
Distinct75
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2935.77381
Minimum6
Maximum52743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:57.994563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20.45
Q177.75
median470.5
Q31020
95-th percentile17835.85
Maximum52743
Range52737
Interquartile range (IQR)942.25

Descriptive statistics

Standard deviation8964.92715
Coefficient of variation (CV)3.053684559
Kurtosis20.02953006
Mean2935.77381
Median Absolute Deviation (MAD)419
Skewness4.429219637
Sum246605
Variance80369918.8
MonotonicityNot monotonic
2025-05-15T17:31:58.305062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 4
 
4.8%
20 3
 
3.6%
1020 2
 
2.4%
225 2
 
2.4%
125 2
 
2.4%
51 2
 
2.4%
52743 1
 
1.2%
98 1
 
1.2%
606 1
 
1.2%
326 1
 
1.2%
Other values (65) 65
77.4%
ValueCountFrequency (%)
6 1
 
1.2%
9 1
 
1.2%
20 3
3.6%
23 1
 
1.2%
27 1
 
1.2%
ValueCountFrequency (%)
52743 1
1.2%
47493 1
1.2%
36593 1
1.2%
19795 1
1.2%
19606 1
1.2%
Distinct77
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3219.678571
Minimum6
Maximum60047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:58.636106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile28.2
Q182
median489
Q31047
95-th percentile7651.4
Maximum60047
Range60041
Interquartile range (IQR)965

Descriptive statistics

Standard deviation8670.472883
Coefficient of variation (CV)3.3472168
Kurtosis31.13882027
Mean2590.352941
Median Absolute Deviation (MAD)415
Skewness5.418457547
Sum220180
Variance75177100.02
MonotonicityNot monotonic
2025-05-18T21:34:49.581805image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile28.15
Q185.75
median474.5
Q31060.75
95-th percentile17835.85
Maximum60047
Range60041
Interquartile range (IQR)975

Descriptive statistics

Standard deviation10291.49514
Coefficient of variation (CV)3.19643558
Kurtosis20.48585782
Mean3219.678571
Median Absolute Deviation (MAD)411
Skewness4.520505544
Sum270453
Variance105914872.2
MonotonicityNot monotonic
2025-05-15T17:31:58.862660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile28.15
Q185.75
median474.5
Q31060.75
95-th percentile17835.85
Maximum60047
Range60041
Interquartile range (IQR)975

Descriptive statistics

Standard deviation10291.49514
Coefficient of variation (CV)3.19643558
Kurtosis20.48585782
Mean3219.678571
Median Absolute Deviation (MAD)411
Skewness4.520505544
Sum270453
Variance105914872.2
MonotonicityNot monotonic
2025-05-15T17:31:58.862660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 4
 
4.7%
225 2
 
2.4%
1105 2
 
2.4%
125 2
 
2.4%
51 2
 
2.4%
52 1
 
1.2%
667 1
 
1.2%
4393 1
 
1.2%
98 1
 
1.2%
606 1
 
1.2%
Other values (68) 68
80.0%
ValueCountFrequency (%)
6 1
1.2%
9 1
1.2%
23 1
1.2%
27 1
1.2%
28 1
1.2%
ValueCountFrequency (%)
60047 1
1.2%
47493 1
1.2%
19795 1
1.2%
19606 1
1.2%
7805 1
1.2%

project_prf_normalised_level_1_pdr_ufp
Real number (ℝ)

Missing 

Distinct63
Distinct (%)75.0%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean12.24880952
Minimum0.2
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:49.829548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 4
 
4.8%
225 2
 
2.4%
51 2
 
2.4%
1105 2
 
2.4%
125 2
 
2.4%
606 1
 
1.2%
326 1
 
1.2%
600 1
 
1.2%
667 1
 
1.2%
52743 1
 
1.2%
Other values (67) 67
79.8%
ValueCountFrequency (%)
6 1
1.2%
9 1
1.2%
23 1
1.2%
27 1
1.2%
28 1
1.2%
ValueCountFrequency (%)
60047 1
1.2%
52743 1
1.2%
47493 1
1.2%
19795 1
1.2%
19606 1
1.2%
Distinct65
Distinct (%)78.3%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean12.91204819
Minimum0.1
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:59.096674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.6
Q12.25
median3.5
Q311.075
95-th percentile45.03
Maximum171
Range170.8
Interquartile range (IQR)8.825

Descriptive statistics

Standard deviation23.75301373
Coefficient of variation (CV)1.939209985
Kurtosis25.72346256
Mean12.24880952
Median Absolute Deviation (MAD)1.85
Skewness4.526519351
Sum1028.9
Variance564.2056612
MonotonicityNot monotonic
2025-05-18T21:34:50.056080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.51
Q12.35
median3.6
Q312.1
95-th percentile47.37
Maximum171
Range170.9
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation24.1564484
Coefficient of variation (CV)1.870845589
Kurtosis23.75351455
Mean12.91204819
Median Absolute Deviation (MAD)2.1
Skewness4.313646939
Sum1071.7
Variance583.5339994
MonotonicityNot monotonic
2025-05-15T17:31:59.306380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.51
Q12.35
median3.6
Q312.1
95-th percentile47.37
Maximum171
Range170.9
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation24.1564484
Coefficient of variation (CV)1.870845589
Kurtosis23.75351455
Mean12.91204819
Median Absolute Deviation (MAD)2.1
Skewness4.313646939
Sum1071.7
Variance583.5339994
MonotonicityNot monotonic
2025-05-15T17:31:59.306380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 3
 
3.5%
2.8 3
 
3.5%
2.6 3
 
3.5%
2 2
 
2.4%
3.1 2
 
2.4%
2.1 2
 
2.4%
0.6 2
 
2.4%
3.6 2
 
2.4%
4.4 2
 
2.4%
2.7 2
 
2.4%
Other values (53) 61
71.8%
ValueCountFrequency (%)
0.2 1
1.2%
0.4 2
2.4%
0.5 1
1.2%
0.6 2
2.4%
0.7 2
2.4%
ValueCountFrequency (%)
171 1
1.2%
102 1
1.2%
53.8 1
1.2%
48.2 1
1.2%
45.3 1
1.2%

project_prf_normalised_pdr_ufp
Real number (ℝ)

Missing 

Distinct62
Distinct (%)73.8%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean12.28095238
Minimum0.5
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:50.413146image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 3
 
3.6%
3.5 3
 
3.6%
2 2
 
2.4%
3 2
 
2.4%
3.1 2
 
2.4%
2.1 2
 
2.4%
3.6 2
 
2.4%
4.4 2
 
2.4%
3.9 2
 
2.4%
2.6 2
 
2.4%
Other values (55) 61
72.6%
ValueCountFrequency (%)
0.1 1
1.2%
0.2 1
1.2%
0.4 2
2.4%
0.5 1
1.2%
0.6 1
1.2%
ValueCountFrequency (%)
171 1
1.2%
102 1
1.2%
53.8 1
1.2%
48.2 1
1.2%
47.6 1
1.2%

Project (PRF) - Normalised PDR (ufp)
Real number (ℝ)

Missing 

Distinct64
Distinct (%)77.1%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean12.94457831
Minimum0.1
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:59.506655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 3
 
3.6%
3.5 3
 
3.6%
2 2
 
2.4%
3 2
 
2.4%
3.1 2
 
2.4%
2.1 2
 
2.4%
3.6 2
 
2.4%
4.4 2
 
2.4%
3.9 2
 
2.4%
2.6 2
 
2.4%
Other values (55) 61
72.6%
ValueCountFrequency (%)
0.1 1
1.2%
0.2 1
1.2%
0.4 2
2.4%
0.5 1
1.2%
0.6 1
1.2%
ValueCountFrequency (%)
171 1
1.2%
102 1
1.2%
53.8 1
1.2%
48.2 1
1.2%
47.6 1
1.2%

Project (PRF) - Normalised PDR (ufp)
Real number (ℝ)

Missing 

Distinct64
Distinct (%)77.1%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean12.94457831
Minimum0.1
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:59.506655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.7
Q12.275
median3.5
Q311.075
95-th percentile45.03
Maximum171
Range170.5
Interquartile range (IQR)8.8

Descriptive statistics

Standard deviation23.73769549
Coefficient of variation (CV)1.932887186
Kurtosis25.77157473
Mean12.28095238
Median Absolute Deviation (MAD)1.8
Skewness4.532061556
Sum1031.6
Variance563.478187
MonotonicityNot monotonic
2025-05-18T21:34:50.651987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.7
Q12.45
median3.6
Q312.1
95-th percentile47.37
Maximum171
Range170.9
Interquartile range (IQR)9.65

Descriptive statistics

Standard deviation24.14029752
Coefficient of variation (CV)1.864896402
Kurtosis23.80033253
Mean12.94457831
Median Absolute Deviation (MAD)1.9
Skewness4.319177978
Sum1074.4
Variance582.7539641
MonotonicityNot monotonic
2025-05-15T17:31:59.741542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.7
Q12.45
median3.6
Q312.1
95-th percentile47.37
Maximum171
Range170.9
Interquartile range (IQR)9.65

Descriptive statistics

Standard deviation24.14029752
Coefficient of variation (CV)1.864896402
Kurtosis23.80033253
Mean12.94457831
Median Absolute Deviation (MAD)1.9
Skewness4.319177978
Sum1074.4
Variance582.7539641
MonotonicityNot monotonic
2025-05-15T17:31:59.741542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.6 4
 
4.7%
2.8 3
 
3.5%
3.5 3
 
3.5%
0.6 3
 
3.5%
2 2
 
2.4%
1.7 2
 
2.4%
1.9 2
 
2.4%
3.6 2
 
2.4%
0.7 2
 
2.4%
28.2 2
 
2.4%
Other values (52) 59
69.4%
ValueCountFrequency (%)
0.5 1
 
1.2%
0.6 3
3.5%
0.7 2
2.4%
0.8 1
 
1.2%
0.9 1
 
1.2%
ValueCountFrequency (%)
171 1
1.2%
102 1
1.2%
53.8 1
1.2%
48.2 1
1.2%
45.3 1
1.2%

project_prf_defect_density
Real number (ℝ)

Missing  Zeros 

Distinct13
Distinct (%)43.3%
Missing55
Missing (%)64.7%
Infinite0
Infinite (%)0.0%
Mean19.80666667
Minimum0
Maximum173.9
Zeros18
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:50.832738image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 3
 
3.6%
2.6 3
 
3.6%
3.5 3
 
3.6%
2 2
 
2.4%
3 2
 
2.4%
3.1 2
 
2.4%
3.6 2
 
2.4%
4.4 2
 
2.4%
2.7 2
 
2.4%
1.9 2
 
2.4%
Other values (54) 60
71.4%
ValueCountFrequency (%)
0.1 1
1.2%
0.5 1
1.2%
0.6 2
2.4%
0.7 2
2.4%
0.8 1
1.2%
ValueCountFrequency (%)
171 1
1.2%
102 1
1.2%
53.8 1
1.2%
48.2 1
1.2%
47.6 1
1.2%

Project (PRF) - Defect Density
Real number (ℝ)

Missing  Zeros 

Distinct14
Distinct (%)45.2%
Missing53
Missing (%)63.1%
Infinite0
Infinite (%)0.0%
Mean19.22903226
Minimum0
Maximum173.9
Zeros18
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:31:59.921728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.75
95-th percentile57.74
Maximum173.9
Range173.9
Interquartile range (IQR)37.75

Descriptive statistics

Standard deviation36.0868104
Coefficient of variation (CV)1.82195273
Kurtosis11.02197841
Mean19.80666667
Median Absolute Deviation (MAD)0
Skewness2.940388829
Sum594.2
Variance1302.257885
MonotonicityNot monotonic
2025-05-18T21:34:51.024084image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q332.7
95-th percentile57.6
Maximum173.9
Range173.9
Interquartile range (IQR)32.7

Descriptive statistics

Standard deviation35.62573221
Coefficient of variation (CV)1.852705416
Kurtosis11.41004597
Mean19.22903226
Median Absolute Deviation (MAD)0
Skewness2.994856081
Sum596.1
Variance1269.192796
MonotonicityNot monotonic
2025-05-15T17:32:00.174671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 18
 
21.2%
56.2 1
 
1.2%
47.6 1
 
1.2%
43.5 1
 
1.2%
59 1
 
1.2%
173.9 1
 
1.2%
21.3 1
 
1.2%
51.5 1
 
1.2%
42.8 1
 
1.2%
9.2 1
 
1.2%
Other values (3) 3
 
3.5%
(Missing) 55
64.7%
ValueCountFrequency (%)
0 18
21.2%
9.2 1
 
1.2%
14 1
 
1.2%
21.3 1
 
1.2%
22.6 1
 
1.2%
ValueCountFrequency (%)
173.9 1
1.2%
59 1
1.2%
56.2 1
1.2%
52.6 1
1.2%
51.5 1
1.2%

project_prf_speed_of_delivery
Real number (ℝ)

Missing 

Distinct75
Distinct (%)91.5%
Missing3
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean59.98292683
Minimum0.2
Maximum345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:51.343660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 18
 
21.4%
56.2 1
 
1.2%
47.6 1
 
1.2%
43.5 1
 
1.2%
59 1
 
1.2%
173.9 1
 
1.2%
21.3 1
 
1.2%
51.5 1
 
1.2%
42.8 1
 
1.2%
9.2 1
 
1.2%
Other values (4) 4
 
4.8%
(Missing) 53
63.1%
ValueCountFrequency (%)
0 18
21.4%
1.9 1
 
1.2%
9.2 1
 
1.2%
14 1
 
1.2%
21.3 1
 
1.2%
ValueCountFrequency (%)
173.9 1
1.2%
59 1
1.2%
56.2 1
1.2%
52.6 1
1.2%
51.5 1
1.2%

Project (PRF) - Speed of Delivery
Real number (ℝ)

Missing 

Distinct75
Distinct (%)92.6%
Missing3
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean83.38395062
Minimum0.2
Maximum2157.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:00.412799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 18
 
21.4%
56.2 1
 
1.2%
47.6 1
 
1.2%
43.5 1
 
1.2%
59 1
 
1.2%
173.9 1
 
1.2%
21.3 1
 
1.2%
51.5 1
 
1.2%
42.8 1
 
1.2%
9.2 1
 
1.2%
Other values (4) 4
 
4.8%
(Missing) 53
63.1%
ValueCountFrequency (%)
0 18
21.4%
1.9 1
 
1.2%
9.2 1
 
1.2%
14 1
 
1.2%
21.3 1
 
1.2%
ValueCountFrequency (%)
173.9 1
1.2%
59 1
1.2%
56.2 1
1.2%
52.6 1
1.2%
51.5 1
1.2%

Project (PRF) - Speed of Delivery
Real number (ℝ)

Missing 

Distinct75
Distinct (%)92.6%
Missing3
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean83.38395062
Minimum0.2
Maximum2157.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:00.412799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.805
Q12.725
median28.85
Q372.9
95-th percentile219.625
Maximum345
Range344.8
Interquartile range (IQR)70.175

Descriptive statistics

Standard deviation78.6888572
Coefficient of variation (CV)1.311854245
Kurtosis3.192226824
Mean59.98292683
Median Absolute Deviation (MAD)27.3
Skewness1.846376011
Sum4918.6
Variance6191.936248
MonotonicityNot monotonic
2025-05-18T21:34:51.750871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.8
Q13.1
median29.1
Q372.9
95-th percentile221.4
Maximum2157.2
Range2157
Interquartile range (IQR)69.8

Descriptive statistics

Standard deviation244.4077175
Coefficient of variation (CV)2.93111223
Kurtosis66.75809236
Mean83.38395062
Median Absolute Deviation (MAD)27.5
Skewness7.846185418
Sum6754.1
Variance59735.13236
MonotonicityNot monotonic
2025-05-15T17:32:00.634725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.8
Q13.1
median29.1
Q372.9
95-th percentile221.4
Maximum2157.2
Range2157
Interquartile range (IQR)69.8

Descriptive statistics

Standard deviation244.4077175
Coefficient of variation (CV)2.93111223
Kurtosis66.75809236
Mean83.38395062
Median Absolute Deviation (MAD)27.5
Skewness7.846185418
Sum6754.1
Variance59735.13236
MonotonicityNot monotonic
2025-05-15T17:32:00.634725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3 3
 
3.5%
0.8 2
 
2.4%
1.5 2
 
2.4%
1.1 2
 
2.4%
72.9 2
 
2.4%
121.5 2
 
2.4%
49.9 1
 
1.2%
51.7 1
 
1.2%
14.1 1
 
1.2%
22.9 1
 
1.2%
Other values (65) 65
76.5%
(Missing) 3
 
3.5%
ValueCountFrequency (%)
0.2 1
1.2%
0.3 1
1.2%
0.4 1
1.2%
0.8 2
2.4%
0.9 1
1.2%
ValueCountFrequency (%)
345 1
1.2%
342.8 1
1.2%
249.3 1
1.2%
237.5 1
1.2%
220 1
1.2%

project_prf_manpower_delivery_rate
Real number (ℝ)

Missing 

Distinct44
Distinct (%)80.0%
Missing30
Missing (%)35.3%
Infinite0
Infinite (%)0.0%
Mean7.603636364
Minimum0.2
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:52.015604image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3 2
 
2.4%
0.8 2
 
2.4%
1.5 2
 
2.4%
1.1 2
 
2.4%
72.9 2
 
2.4%
121.5 2
 
2.4%
49.9 1
 
1.2%
51.7 1
 
1.2%
14.1 1
 
1.2%
74.3 1
 
1.2%
Other values (65) 65
77.4%
(Missing) 3
 
3.6%
ValueCountFrequency (%)
0.2 1
1.2%
0.3 1
1.2%
0.4 1
1.2%
0.8 2
2.4%
0.9 1
1.2%
ValueCountFrequency (%)
2157.2 1
1.2%
345 1
1.2%
249.3 1
1.2%
237.5 1
1.2%
221.4 1
1.2%

Project (PRF) - Manpower Delivery Rate
Real number (ℝ)

Missing 

Distinct46
Distinct (%)82.1%
Missing28
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean46.05178571
Minimum0.2
Maximum2157.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:00.825005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3 2
 
2.4%
0.8 2
 
2.4%
1.5 2
 
2.4%
1.1 2
 
2.4%
72.9 2
 
2.4%
121.5 2
 
2.4%
49.9 1
 
1.2%
51.7 1
 
1.2%
14.1 1
 
1.2%
74.3 1
 
1.2%
Other values (65) 65
77.4%
(Missing) 3
 
3.6%
ValueCountFrequency (%)
0.2 1
1.2%
0.3 1
1.2%
0.4 1
1.2%
0.8 2
2.4%
0.9 1
1.2%
ValueCountFrequency (%)
2157.2 1
1.2%
345 1
1.2%
249.3 1
1.2%
237.5 1
1.2%
221.4 1
1.2%

Project (PRF) - Manpower Delivery Rate
Real number (ℝ)

Missing 

Distinct46
Distinct (%)82.1%
Missing28
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean46.05178571
Minimum0.2
Maximum2157.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:00.825005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.68
Q11.3
median2.6
Q39.5
95-th percentile18.6
Maximum82
Range81.8
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation14.42174238
Coefficient of variation (CV)1.896690174
Kurtosis18.9381919
Mean7.603636364
Median Absolute Deviation (MAD)1.8
Skewness4.209259157
Sum418.2
Variance207.9866532
MonotonicityNot monotonic
2025-05-18T21:34:52.259493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.7
Q11.375
median2.95
Q39.575
95-th percentile35.725
Maximum2157.2
Range2157
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation287.5976154
Coefficient of variation (CV)6.245091496
Kurtosis55.70915661
Mean46.05178571
Median Absolute Deviation (MAD)2
Skewness7.455195178
Sum2578.9
Variance82712.38836
MonotonicityNot monotonic
2025-05-15T17:32:01.009339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1.3 4
 
4.7%
1.5 3
 
3.5%
0.8 3
 
3.5%
9.5 3
 
3.5%
2 2
 
2.4%
1.1 2
 
2.4%
2.2 1
 
1.2%
14.9 1
 
1.2%
4.5 1
 
1.2%
10.3 1
 
1.2%
Other values (34) 34
40.0%
(Missing) 30
35.3%
ValueCountFrequency (%)
0.2 1
 
1.2%
0.3 1
 
1.2%
0.4 1
 
1.2%
0.8 3
3.5%
0.9 1
 
1.2%
ValueCountFrequency (%)
82 1
1.2%
70.3 1
1.2%
24.2 1
1.2%
16.2 1
1.2%
14.9 1
1.2%

project_prf_project_elapsed_time
Real number (ℝ)

Missing 

Distinct20
Distinct (%)24.1%
Missing2
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean6.506024096
Minimum0.4
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:52.522763image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
9.5 3
 
3.6%
0.8 3
 
3.6%
1.3 3
 
3.6%
1.5 3
 
3.6%
2 2
 
2.4%
1.1 2
 
2.4%
82 1
 
1.2%
14.9 1
 
1.2%
4.5 1
 
1.2%
10.3 1
 
1.2%
Other values (36) 36
42.9%
(Missing) 28
33.3%
ValueCountFrequency (%)
0.2 1
 
1.2%
0.3 1
 
1.2%
0.4 1
 
1.2%
0.8 3
3.6%
0.9 1
 
1.2%
ValueCountFrequency (%)
2157.2 1
1.2%
82 1
1.2%
70.3 1
1.2%
24.2 1
1.2%
16.2 1
1.2%

Project (PRF) - Project Elapsed Time
Real number (ℝ)

Missing 

Distinct21
Distinct (%)25.6%
Missing2
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean6.420731707
Minimum0.4
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:01.174815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.4
Q13
median4
Q312
95-th percentile12
Maximum19
Range18.6
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.025358849
Coefficient of variation (CV)0.7724162675
Kurtosis-1.125420457
Mean6.506024096
Median Absolute Deviation (MAD)3.6
Skewness0.4366792968
Sum540
Variance25.25423156
MonotonicityNot monotonic
2025-05-18T21:34:52.724916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.4
Q12.625
median4
Q312
95-th percentile12
Maximum19
Range18.6
Interquartile range (IQR)9.375

Descriptive statistics

Standard deviation5.03056676
Coefficient of variation (CV)0.7834880804
Kurtosis-1.091461644
Mean6.420731707
Median Absolute Deviation (MAD)3.45
Skewness0.4716632628
Sum526.5
Variance25.30660193
MonotonicityNot monotonic
2025-05-15T17:32:01.377627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
12 24
28.2%
3 19
22.4%
0.4 7
 
8.2%
5 4
 
4.7%
2 4
 
4.7%
0.7 4
 
4.7%
8 3
 
3.5%
4 3
 
3.5%
7 2
 
2.4%
10 2
 
2.4%
Other values (10) 11
12.9%
ValueCountFrequency (%)
0.4 7
8.2%
0.7 4
4.7%
1 1
 
1.2%
1.1 1
 
1.2%
1.4 1
 
1.2%
ValueCountFrequency (%)
19 1
 
1.2%
18 1
 
1.2%
15 1
 
1.2%
14.8 1
 
1.2%
12 24
28.2%
Distinct9
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:52.935978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
12 23
27.4%
3 19
22.6%
0.4 7
 
8.3%
5 4
 
4.8%
2 4
 
4.8%
0.7 4
 
4.8%
8 3
 
3.6%
9 2
 
2.4%
4 2
 
2.4%
10 2
 
2.4%
Other values (11) 12
14.3%
ValueCountFrequency (%)
0.4 7
8.3%
0.7 4
4.8%
1 1
 
1.2%
1.1 1
 
1.2%
1.4 1
 
1.2%
ValueCountFrequency (%)
19 1
 
1.2%
18 1
 
1.2%
15 1
 
1.2%
14.8 1
 
1.2%
12 23
27.4%
Distinct9
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:01.520535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length5
Mean length3.682352941
Min length1

Characters and Unicode

Total characters313
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.4%

Sample

1st row1
2nd rowMissing
3rd rowMissing
4th row9-14
5th row1
ValueCountFrequency (%)
missing 27
31.8%
1 22
25.9%
5-8 12
14.1%
2 9
 
10.6%
3-4 7
 
8.2%
9-14 4
 
4.7%
21-30 2
 
2.4%
41-50 1
 
1.2%
61-70 1
 
1.2%
2025-05-18T21:34:53.387110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length5
Mean length3.619047619
Min length1

Characters and Unicode

Total characters304
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row1
2nd rowMissing
3rd rowMissing
4th row9-14
5th row1
ValueCountFrequency (%)
missing 25
29.8%
1 22
26.2%
5-8 12
14.3%
2 9
 
10.7%
3-4 7
 
8.3%
9-14 4
 
4.8%
21-30 2
 
2.4%
41-50 2
 
2.4%
61-70 1
 
1.2%
2025-05-15T17:32:01.877624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length5
Mean length3.619047619
Min length1

Characters and Unicode

Total characters304
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row1
2nd rowMissing
3rd rowMissing
4th row9-14
5th row1
ValueCountFrequency (%)
missing 25
29.8%
1 22
26.2%
5-8 12
14.3%
2 9
 
10.7%
3-4 7
 
8.3%
9-14 4
 
4.8%
21-30 2
 
2.4%
41-50 2
 
2.4%
61-70 1
 
1.2%
2025-05-15T17:32:01.877624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 54
17.3%
s 54
17.3%
1 30
9.6%
M 27
8.6%
n 27
8.6%
g 27
8.6%
- 27
8.6%
5 13
 
4.2%
8 12
 
3.8%
4 12
 
3.8%
Other values (6) 30
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 54
17.3%
s 54
17.3%
1 30
9.6%
M 27
8.6%
n 27
8.6%
g 27
8.6%
- 27
8.6%
5 13
 
4.2%
8 12
 
3.8%
4 12
 
3.8%
Other values (6) 30
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 54
17.3%
s 54
17.3%
1 30
9.6%
M 27
8.6%
n 27
8.6%
g 27
8.6%
- 27
8.6%
5 13
 
4.2%
8 12
 
3.8%
4 12
 
3.8%
Other values (6) 30
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 54
17.3%
s 54
17.3%
1 30
9.6%
M 27
8.6%
n 27
8.6%
g 27
8.6%
- 27
8.6%
5 13
 
4.2%
8 12
 
3.8%
4 12
 
3.8%
Other values (6) 30
9.6%

project_prf_max_team_size
Real number (ℝ)

Missing 

Distinct13
Distinct (%)22.4%
Missing27
Missing (%)31.8%
Infinite0
Infinite (%)0.0%
Mean5.672413793
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:53.561306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 50
16.4%
s 50
16.4%
1 31
10.2%
- 28
9.2%
M 25
8.2%
n 25
8.2%
g 25
8.2%
5 14
 
4.6%
4 13
 
4.3%
8 12
 
3.9%
Other values (6) 31
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 50
16.4%
s 50
16.4%
1 31
10.2%
- 28
9.2%
M 25
8.2%
n 25
8.2%
g 25
8.2%
5 14
 
4.6%
4 13
 
4.3%
8 12
 
3.9%
Other values (6) 31
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 50
16.4%
s 50
16.4%
1 31
10.2%
- 28
9.2%
M 25
8.2%
n 25
8.2%
g 25
8.2%
5 14
 
4.6%
4 13
 
4.3%
8 12
 
3.9%
Other values (6) 31
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 50
16.4%
s 50
16.4%
1 31
10.2%
- 28
9.2%
M 25
8.2%
n 25
8.2%
g 25
8.2%
5 14
 
4.6%
4 13
 
4.3%
8 12
 
3.9%
Other values (6) 31
10.2%

Project (PRF) - Max Team Size
Real number (ℝ)

Missing 

Distinct14
Distinct (%)23.7%
Missing25
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean6.355932203
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:02.113659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 50
16.4%
s 50
16.4%
1 31
10.2%
- 28
9.2%
M 25
8.2%
n 25
8.2%
g 25
8.2%
5 14
 
4.6%
4 13
 
4.3%
8 12
 
3.9%
Other values (6) 31
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 50
16.4%
s 50
16.4%
1 31
10.2%
- 28
9.2%
M 25
8.2%
n 25
8.2%
g 25
8.2%
5 14
 
4.6%
4 13
 
4.3%
8 12
 
3.9%
Other values (6) 31
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 50
16.4%
s 50
16.4%
1 31
10.2%
- 28
9.2%
M 25
8.2%
n 25
8.2%
g 25
8.2%
5 14
 
4.6%
4 13
 
4.3%
8 12
 
3.9%
Other values (6) 31
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 50
16.4%
s 50
16.4%
1 31
10.2%
- 28
9.2%
M 25
8.2%
n 25
8.2%
g 25
8.2%
5 14
 
4.6%
4 13
 
4.3%
8 12
 
3.9%
Other values (6) 31
10.2%

Project (PRF) - Max Team Size
Real number (ℝ)

Missing 

Distinct14
Distinct (%)23.7%
Missing25
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean6.355932203
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:02.113659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile22.6
Maximum68
Range67
Interquartile range (IQR)4

Descriptive statistics

Standard deviation10.70724944
Coefficient of variation (CV)1.887600205
Kurtosis21.90482408
Mean5.672413793
Median Absolute Deviation (MAD)1
Skewness4.390223236
Sum329
Variance114.6451906
MonotonicityNot monotonic
2025-05-18T21:34:53.770483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile27.5
Maximum68
Range67
Interquartile range (IQR)4

Descriptive statistics

Standard deviation11.84200984
Coefficient of variation (CV)1.863142881
Kurtosis14.90606172
Mean6.355932203
Median Absolute Deviation (MAD)1
Skewness3.712722913
Sum375
Variance140.233197
MonotonicityNot monotonic
2025-05-15T17:32:02.354687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 22
25.9%
5 10
 
11.8%
2 9
 
10.6%
4 5
 
5.9%
8 2
 
2.4%
3 2
 
2.4%
10 2
 
2.4%
9 1
 
1.2%
22 1
 
1.2%
26 1
 
1.2%
Other values (3) 3
 
3.5%
(Missing) 27
31.8%
ValueCountFrequency (%)
1 22
25.9%
2 9
10.6%
3 2
 
2.4%
4 5
 
5.9%
5 10
11.8%
ValueCountFrequency (%)
68 1
1.2%
41 1
1.2%
26 1
1.2%
22 1
1.2%
11 1
1.2%
Distinct8
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:53.957838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 22
26.2%
5 10
 
11.9%
2 9
 
10.7%
4 5
 
6.0%
8 2
 
2.4%
3 2
 
2.4%
10 2
 
2.4%
9 1
 
1.2%
22 1
 
1.2%
26 1
 
1.2%
Other values (4) 4
 
4.8%
(Missing) 25
29.8%
ValueCountFrequency (%)
1 22
26.2%
2 9
10.7%
3 2
 
2.4%
4 5
 
6.0%
5 10
11.9%
ValueCountFrequency (%)
68 1
1.2%
46 1
1.2%
41 1
1.2%
26 1
1.2%
22 1
1.2%
Distinct10
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:02.541479image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 22
26.2%
5 10
 
11.9%
2 9
 
10.7%
4 5
 
6.0%
8 2
 
2.4%
3 2
 
2.4%
10 2
 
2.4%
9 1
 
1.2%
22 1
 
1.2%
26 1
 
1.2%
Other values (4) 4
 
4.8%
(Missing) 25
29.8%
ValueCountFrequency (%)
1 22
26.2%
2 9
10.7%
3 2
 
2.4%
4 5
 
6.0%
5 10
11.9%
ValueCountFrequency (%)
68 1
1.2%
46 1
1.2%
41 1
1.2%
26 1
1.2%
22 1
1.2%
Distinct10
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:02.541479image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length77
Median length17
Mean length23
Min length7

Characters and Unicode

Total characters1955
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)4.7%

Sample

1st rowagile development
2nd rowagile development
3rd rowagile development
4th rowagile development
5th rowagile development
ValueCountFrequency (%)
development 77
33.2%
agile 76
32.8%
process 23
 
9.9%
unified 12
 
5.2%
personal 11
 
4.7%
software 11
 
4.7%
psp 11
 
4.7%
missing 4
 
1.7%
scrum 1
 
0.4%
iterative 1
 
0.4%
Other values (5) 5
 
2.2%
2025-05-18T21:34:54.385362image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length131
Median length18
Mean length25.44047619
Min length7

Characters and Unicode

Total characters2137
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)7.1%

Sample

1st rowAgile Development;
2nd rowAgile Development;
3rd rowAgile Development;
4th rowAgile Development;
5th rowAgile Development;
ValueCountFrequency (%)
agile 75
34.7%
development 65
30.1%
process 23
 
10.6%
software 11
 
5.1%
psp);unified 11
 
5.1%
development;personal 6
 
2.8%
personal 5
 
2.3%
missing 4
 
1.9%
application 2
 
0.9%
teams 1
 
0.5%
Other values (13) 13
 
6.0%
2025-05-15T17:32:03.049991image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length131
Median length18
Mean length25.44047619
Min length7

Characters and Unicode

Total characters2137
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)7.1%

Sample

1st rowAgile Development;
2nd rowAgile Development;
3rd rowAgile Development;
4th rowAgile Development;
5th rowAgile Development;
ValueCountFrequency (%)
agile 75
34.7%
development 65
30.1%
process 23
 
10.6%
software 11
 
5.1%
psp);unified 11
 
5.1%
development;personal 6
 
2.8%
personal 5
 
2.3%
missing 4
 
1.9%
application 2
 
0.9%
teams 1
 
0.5%
Other values (13) 13
 
6.0%
2025-05-15T17:32:03.049991image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 367
18.8%
l 167
 
8.5%
147
 
7.5%
p 135
 
6.9%
o 125
 
6.4%
i 115
 
5.9%
n 108
 
5.5%
a 104
 
5.3%
t 95
 
4.9%
d 90
 
4.6%
Other values (14) 502
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 367
18.8%
l 167
 
8.5%
147
 
7.5%
p 135
 
6.9%
o 125
 
6.4%
i 115
 
5.9%
n 108
 
5.5%
a 104
 
5.3%
t 95
 
4.9%
d 90
 
4.6%
Other values (14) 502
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 367
18.8%
l 167
 
8.5%
147
 
7.5%
p 135
 
6.9%
o 125
 
6.4%
i 115
 
5.9%
n 108
 
5.5%
a 104
 
5.3%
t 95
 
4.9%
d 90
 
4.6%
Other values (14) 502
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 367
18.8%
l 167
 
8.5%
147
 
7.5%
p 135
 
6.9%
o 125
 
6.4%
i 115
 
5.9%
n 108
 
5.5%
a 104
 
5.3%
t 95
 
4.9%
d 90
 
4.6%
Other values (14) 502
25.7%

process_pmf_docs
Real number (ℝ)

Distinct12
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.176470588
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:54.603236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 374
17.5%
l 169
 
7.9%
132
 
6.2%
o 130
 
6.1%
i 124
 
5.8%
n 114
 
5.3%
; 111
 
5.2%
t 100
 
4.7%
m 86
 
4.0%
p 82
 
3.8%
Other values (27) 715
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 374
17.5%
l 169
 
7.9%
132
 
6.2%
o 130
 
6.1%
i 124
 
5.8%
n 114
 
5.3%
; 111
 
5.2%
t 100
 
4.7%
m 86
 
4.0%
p 82
 
3.8%
Other values (27) 715
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 374
17.5%
l 169
 
7.9%
132
 
6.2%
o 130
 
6.1%
i 124
 
5.8%
n 114
 
5.3%
; 111
 
5.2%
t 100
 
4.7%
m 86
 
4.0%
p 82
 
3.8%
Other values (27) 715
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 374
17.5%
l 169
 
7.9%
132
 
6.2%
o 130
 
6.1%
i 124
 
5.8%
n 114
 
5.3%
; 111
 
5.2%
t 100
 
4.7%
m 86
 
4.0%
p 82
 
3.8%
Other values (27) 715
33.5%

Process (PMF) - Docs
Real number (ℝ)

Distinct13
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.511904762
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:03.277387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 374
17.5%
l 169
 
7.9%
132
 
6.2%
o 130
 
6.1%
i 124
 
5.8%
n 114
 
5.3%
; 111
 
5.2%
t 100
 
4.7%
m 86
 
4.0%
p 82
 
3.8%
Other values (27) 715
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 374
17.5%
l 169
 
7.9%
132
 
6.2%
o 130
 
6.1%
i 124
 
5.8%
n 114
 
5.3%
; 111
 
5.2%
t 100
 
4.7%
m 86
 
4.0%
p 82
 
3.8%
Other values (27) 715
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 374
17.5%
l 169
 
7.9%
132
 
6.2%
o 130
 
6.1%
i 124
 
5.8%
n 114
 
5.3%
; 111
 
5.2%
t 100
 
4.7%
m 86
 
4.0%
p 82
 
3.8%
Other values (27) 715
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 374
17.5%
l 169
 
7.9%
132
 
6.2%
o 130
 
6.1%
i 124
 
5.8%
n 114
 
5.3%
; 111
 
5.2%
t 100
 
4.7%
m 86
 
4.0%
p 82
 
3.8%
Other values (27) 715
33.5%

Process (PMF) - Docs
Real number (ℝ)

Distinct13
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.511904762
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:03.277387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q317
95-th percentile18
Maximum19
Range18
Interquartile range (IQR)12

Descriptive statistics

Standard deviation5.93451661
Coefficient of variation (CV)0.6467101434
Kurtosis-1.417464828
Mean9.176470588
Median Absolute Deviation (MAD)3
Skewness0.5930214267
Sum780
Variance35.21848739
MonotonicityNot monotonic
2025-05-18T21:34:54.787121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q317
95-th percentile18
Maximum20
Range19
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.066884331
Coefficient of variation (CV)0.6378201299
Kurtosis-1.538101274
Mean9.511904762
Median Absolute Deviation (MAD)3
Skewness0.4826263648
Sum799
Variance36.80708548
MonotonicityNot monotonic
2025-05-15T17:32:03.487815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 25
29.4%
18 15
17.6%
3 14
16.5%
5 12
14.1%
17 6
 
7.1%
16 4
 
4.7%
7 3
 
3.5%
11 2
 
2.4%
14 1
 
1.2%
15 1
 
1.2%
Other values (2) 2
 
2.4%
ValueCountFrequency (%)
1 1
 
1.2%
3 14
16.5%
5 12
14.1%
6 25
29.4%
7 3
 
3.5%
ValueCountFrequency (%)
19 1
 
1.2%
18 15
17.6%
17 6
 
7.1%
16 4
 
4.7%
15 1
 
1.2%
Distinct5
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:55.039937image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6 24
28.6%
18 15
17.9%
3 14
16.7%
5 10
11.9%
17 6
 
7.1%
16 4
 
4.8%
7 3
 
3.6%
15 2
 
2.4%
11 2
 
2.4%
14 1
 
1.2%
Other values (3) 3
 
3.6%
ValueCountFrequency (%)
1 1
 
1.2%
3 14
16.7%
5 10
11.9%
6 24
28.6%
7 3
 
3.6%
ValueCountFrequency (%)
20 1
 
1.2%
19 1
 
1.2%
18 15
17.9%
17 6
 
7.1%
16 4
 
4.8%
Distinct5
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:03.691066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length36
Median length13
Mean length19.04705882
Min length7

Characters and Unicode

Total characters1619
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowstand-alone
2nd rowMissing
3rd rowMissing
4th rowclient-server
5th rowstand-alone
ValueCountFrequency (%)
stand-alone 30
14.9%
multi-tier 30
14.9%
with 29
14.4%
web 29
14.4%
public 29
14.4%
interface 29
14.4%
missing 15
7.5%
client-server 10
 
5.0%
2025-05-18T21:34:55.434447image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length36
Median length13
Mean length18.57142857
Min length7

Characters and Unicode

Total characters1560
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowStand alone
2nd rowMissing
3rd rowMissing
4th rowClient server
5th rowStand alone
ValueCountFrequency (%)
stand 30
12.9%
alone 30
12.9%
multi-tier 28
12.0%
with 27
11.6%
web 27
11.6%
public 27
11.6%
interface 27
11.6%
missing 15
6.4%
client 11
 
4.7%
server 11
 
4.7%
2025-05-15T17:32:04.186775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length36
Median length13
Mean length18.57142857
Min length7

Characters and Unicode

Total characters1560
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowStand alone
2nd rowMissing
3rd rowMissing
4th rowClient server
5th rowStand alone
ValueCountFrequency (%)
stand 30
12.9%
alone 30
12.9%
multi-tier 28
12.0%
with 27
11.6%
web 27
11.6%
public 27
11.6%
interface 27
11.6%
missing 15
6.4%
client 11
 
4.7%
server 11
 
4.7%
2025-05-15T17:32:04.186775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 187
11.6%
e 177
 
10.9%
t 158
 
9.8%
116
 
7.2%
n 114
 
7.0%
l 99
 
6.1%
a 89
 
5.5%
r 79
 
4.9%
s 70
 
4.3%
- 70
 
4.3%
Other values (13) 460
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1619
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 187
11.6%
e 177
 
10.9%
t 158
 
9.8%
116
 
7.2%
n 114
 
7.0%
l 99
 
6.1%
a 89
 
5.5%
r 79
 
4.9%
s 70
 
4.3%
- 70
 
4.3%
Other values (13) 460
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1619
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 187
11.6%
e 177
 
10.9%
t 158
 
9.8%
116
 
7.2%
n 114
 
7.0%
l 99
 
6.1%
a 89
 
5.5%
r 79
 
4.9%
s 70
 
4.3%
- 70
 
4.3%
Other values (13) 460
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1619
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 187
11.6%
e 177
 
10.9%
t 158
 
9.8%
116
 
7.2%
n 114
 
7.0%
l 99
 
6.1%
a 89
 
5.5%
r 79
 
4.9%
s 70
 
4.3%
- 70
 
4.3%
Other values (13) 460
28.4%
Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:55.647518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 178
11.4%
e 172
11.0%
t 151
 
9.7%
149
 
9.6%
n 113
 
7.2%
l 96
 
6.2%
a 87
 
5.6%
r 77
 
4.9%
u 55
 
3.5%
w 54
 
3.5%
Other values (14) 428
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 178
11.4%
e 172
11.0%
t 151
 
9.7%
149
 
9.6%
n 113
 
7.2%
l 96
 
6.2%
a 87
 
5.6%
r 77
 
4.9%
u 55
 
3.5%
w 54
 
3.5%
Other values (14) 428
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 178
11.4%
e 172
11.0%
t 151
 
9.7%
149
 
9.6%
n 113
 
7.2%
l 96
 
6.2%
a 87
 
5.6%
r 77
 
4.9%
u 55
 
3.5%
w 54
 
3.5%
Other values (14) 428
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 178
11.4%
e 172
11.0%
t 151
 
9.7%
149
 
9.6%
n 113
 
7.2%
l 96
 
6.2%
a 87
 
5.6%
r 77
 
4.9%
u 55
 
3.5%
w 54
 
3.5%
Other values (14) 428
27.4%
Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:04.371726image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 178
11.4%
e 172
11.0%
t 151
 
9.7%
149
 
9.6%
n 113
 
7.2%
l 96
 
6.2%
a 87
 
5.6%
r 77
 
4.9%
u 55
 
3.5%
w 54
 
3.5%
Other values (14) 428
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 178
11.4%
e 172
11.0%
t 151
 
9.7%
149
 
9.6%
n 113
 
7.2%
l 96
 
6.2%
a 87
 
5.6%
r 77
 
4.9%
u 55
 
3.5%
w 54
 
3.5%
Other values (14) 428
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 178
11.4%
e 172
11.0%
t 151
 
9.7%
149
 
9.6%
n 113
 
7.2%
l 96
 
6.2%
a 87
 
5.6%
r 77
 
4.9%
u 55
 
3.5%
w 54
 
3.5%
Other values (14) 428
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 178
11.4%
e 172
11.0%
t 151
 
9.7%
149
 
9.6%
n 113
 
7.2%
l 96
 
6.2%
a 87
 
5.6%
r 77
 
4.9%
u 55
 
3.5%
w 54
 
3.5%
Other values (14) 428
27.4%
Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:04.371726image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.552941176
Min length2

Characters and Unicode

Total characters472
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowyes
5th rowMissing
ValueCountFrequency (%)
missing 55
64.7%
yes 27
31.8%
no 3
 
3.5%
2025-05-18T21:34:56.135536image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.428571429
Min length2

Characters and Unicode

Total characters456
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowYes
5th rowMissing
ValueCountFrequency (%)
missing 52
61.9%
yes 28
33.3%
no 4
 
4.8%
2025-05-15T17:32:04.817524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.428571429
Min length2

Characters and Unicode

Total characters456
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowYes
5th rowMissing
ValueCountFrequency (%)
missing 52
61.9%
yes 28
33.3%
no 4
 
4.8%
2025-05-15T17:32:04.817524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 137
29.0%
i 110
23.3%
n 58
12.3%
M 55
11.7%
g 55
11.7%
y 27
 
5.7%
e 27
 
5.7%
o 3
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 137
29.0%
i 110
23.3%
n 58
12.3%
M 55
11.7%
g 55
11.7%
y 27
 
5.7%
e 27
 
5.7%
o 3
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 137
29.0%
i 110
23.3%
n 58
12.3%
M 55
11.7%
g 55
11.7%
y 27
 
5.7%
e 27
 
5.7%
o 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 137
29.0%
i 110
23.3%
n 58
12.3%
M 55
11.7%
g 55
11.7%
y 27
 
5.7%
e 27
 
5.7%
o 3
 
0.6%
Distinct13
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:56.330658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 132
28.9%
i 104
22.8%
M 52
 
11.4%
n 52
 
11.4%
g 52
 
11.4%
Y 28
 
6.1%
e 28
 
6.1%
N 4
 
0.9%
o 4
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 132
28.9%
i 104
22.8%
M 52
 
11.4%
n 52
 
11.4%
g 52
 
11.4%
Y 28
 
6.1%
e 28
 
6.1%
N 4
 
0.9%
o 4
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 132
28.9%
i 104
22.8%
M 52
 
11.4%
n 52
 
11.4%
g 52
 
11.4%
Y 28
 
6.1%
e 28
 
6.1%
N 4
 
0.9%
o 4
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 132
28.9%
i 104
22.8%
M 52
 
11.4%
n 52
 
11.4%
g 52
 
11.4%
Y 28
 
6.1%
e 28
 
6.1%
N 4
 
0.9%
o 4
 
0.9%
Distinct11
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:05.065388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 132
28.9%
i 104
22.8%
M 52
 
11.4%
n 52
 
11.4%
g 52
 
11.4%
Y 28
 
6.1%
e 28
 
6.1%
N 4
 
0.9%
o 4
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 132
28.9%
i 104
22.8%
M 52
 
11.4%
n 52
 
11.4%
g 52
 
11.4%
Y 28
 
6.1%
e 28
 
6.1%
N 4
 
0.9%
o 4
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 132
28.9%
i 104
22.8%
M 52
 
11.4%
n 52
 
11.4%
g 52
 
11.4%
Y 28
 
6.1%
e 28
 
6.1%
N 4
 
0.9%
o 4
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 132
28.9%
i 104
22.8%
M 52
 
11.4%
n 52
 
11.4%
g 52
 
11.4%
Y 28
 
6.1%
e 28
 
6.1%
N 4
 
0.9%
o 4
 
0.9%
Distinct11
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:05.065388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length162
Median length7
Mean length18.45882353
Min length7

Characters and Unicode

Total characters1569
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)8.2%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowhtml/web server; security/authentication
5th rowMissing
ValueCountFrequency (%)
missing 59
34.5%
server 36
21.1%
security/authentication 17
 
9.9%
html/web 15
 
8.8%
database 14
 
8.2%
multi-user 3
 
1.8%
legacy 3
 
1.8%
application 3
 
1.8%
print 2
 
1.2%
mail 2
 
1.2%
Other values (15) 17
 
9.9%
2025-05-18T21:34:56.787221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length119
Median length7
Mean length19.1547619
Min length7

Characters and Unicode

Total characters1609
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)7.1%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowData entry & validation;Data retrieval & presentation;Web/HTML browser;
5th rowMissing
ValueCountFrequency (%)
missing 58
29.6%
17
 
8.7%
browser 16
 
8.2%
web/html 13
 
6.6%
entry 9
 
4.6%
validation;data 8
 
4.1%
retrieval 8
 
4.1%
a 6
 
3.1%
run 6
 
3.1%
computer-human 6
 
3.1%
Other values (18) 49
25.0%
2025-05-15T17:32:05.695799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length119
Median length7
Mean length19.1547619
Min length7

Characters and Unicode

Total characters1609
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)7.1%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowData entry & validation;Data retrieval & presentation;Web/HTML browser;
5th rowMissing
ValueCountFrequency (%)
missing 58
29.6%
17
 
8.7%
browser 16
 
8.2%
web/html 13
 
6.6%
entry 9
 
4.6%
validation;data 8
 
4.1%
retrieval 8
 
4.1%
a 6
 
3.1%
run 6
 
3.1%
computer-human 6
 
3.1%
Other values (18) 49
25.0%
2025-05-15T17:32:05.695799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 197
12.6%
i 191
12.2%
e 150
 
9.6%
t 115
 
7.3%
n 105
 
6.7%
r 98
 
6.2%
a 93
 
5.9%
86
 
5.5%
g 66
 
4.2%
M 59
 
3.8%
Other values (22) 409
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 197
12.6%
i 191
12.2%
e 150
 
9.6%
t 115
 
7.3%
n 105
 
6.7%
r 98
 
6.2%
a 93
 
5.9%
86
 
5.5%
g 66
 
4.2%
M 59
 
3.8%
Other values (22) 409
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 197
12.6%
i 191
12.2%
e 150
 
9.6%
t 115
 
7.3%
n 105
 
6.7%
r 98
 
6.2%
a 93
 
5.9%
86
 
5.5%
g 66
 
4.2%
M 59
 
3.8%
Other values (22) 409
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 197
12.6%
i 191
12.2%
e 150
 
9.6%
t 115
 
7.3%
n 105
 
6.7%
r 98
 
6.2%
a 93
 
5.9%
86
 
5.5%
g 66
 
4.2%
M 59
 
3.8%
Other values (22) 409
26.1%
Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:56.926667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 180
 
11.2%
s 163
 
10.1%
e 127
 
7.9%
n 124
 
7.7%
112
 
7.0%
r 99
 
6.2%
a 94
 
5.8%
t 79
 
4.9%
M 75
 
4.7%
g 66
 
4.1%
Other values (26) 490
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 180
 
11.2%
s 163
 
10.1%
e 127
 
7.9%
n 124
 
7.7%
112
 
7.0%
r 99
 
6.2%
a 94
 
5.8%
t 79
 
4.9%
M 75
 
4.7%
g 66
 
4.1%
Other values (26) 490
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 180
 
11.2%
s 163
 
10.1%
e 127
 
7.9%
n 124
 
7.7%
112
 
7.0%
r 99
 
6.2%
a 94
 
5.8%
t 79
 
4.9%
M 75
 
4.7%
g 66
 
4.1%
Other values (26) 490
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 180
 
11.2%
s 163
 
10.1%
e 127
 
7.9%
n 124
 
7.7%
112
 
7.0%
r 99
 
6.2%
a 94
 
5.8%
t 79
 
4.9%
M 75
 
4.7%
g 66
 
4.1%
Other values (26) 490
30.5%
Distinct13
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:06.002796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 180
 
11.2%
s 163
 
10.1%
e 127
 
7.9%
n 124
 
7.7%
112
 
7.0%
r 99
 
6.2%
a 94
 
5.8%
t 79
 
4.9%
M 75
 
4.7%
g 66
 
4.1%
Other values (26) 490
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 180
 
11.2%
s 163
 
10.1%
e 127
 
7.9%
n 124
 
7.7%
112
 
7.0%
r 99
 
6.2%
a 94
 
5.8%
t 79
 
4.9%
M 75
 
4.7%
g 66
 
4.1%
Other values (26) 490
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 180
 
11.2%
s 163
 
10.1%
e 127
 
7.9%
n 124
 
7.7%
112
 
7.0%
r 99
 
6.2%
a 94
 
5.8%
t 79
 
4.9%
M 75
 
4.7%
g 66
 
4.1%
Other values (26) 490
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 180
 
11.2%
s 163
 
10.1%
e 127
 
7.9%
n 124
 
7.7%
112
 
7.0%
r 99
 
6.2%
a 94
 
5.8%
t 79
 
4.9%
M 75
 
4.7%
g 66
 
4.1%
Other values (26) 490
30.5%
Distinct13
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:06.002796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.4
Min length3

Characters and Unicode

Total characters459
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowMissing
5th rowMissing
ValueCountFrequency (%)
missing 51
60.0%
web 34
40.0%
2025-05-18T21:34:57.344486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length157
Median length7
Mean length18.82142857
Min length7

Characters and Unicode

Total characters1581
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)8.3%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowHTML/web server;Security/authentication;
5th rowMissing
ValueCountFrequency (%)
missing 57
40.7%
database 15
 
10.7%
server;html/web 12
 
8.6%
server;security/authentication 10
 
7.1%
server 6
 
4.3%
security/authentication 6
 
4.3%
html/web 4
 
2.9%
legacy 3
 
2.1%
application 3
 
2.1%
print 2
 
1.4%
Other values (18) 22
 
15.7%
2025-05-15T17:32:06.587308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length157
Median length7
Mean length18.82142857
Min length7

Characters and Unicode

Total characters1581
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)8.3%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowHTML/web server;Security/authentication;
5th rowMissing
ValueCountFrequency (%)
missing 57
40.7%
database 15
 
10.7%
server;html/web 12
 
8.6%
server;security/authentication 10
 
7.1%
server 6
 
4.3%
security/authentication 6
 
4.3%
html/web 4
 
2.9%
legacy 3
 
2.1%
application 3
 
2.1%
print 2
 
1.4%
Other values (18) 22
 
15.7%
2025-05-15T17:32:06.587308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 102
22.2%
s 102
22.2%
M 51
11.1%
n 51
11.1%
g 51
11.1%
w 34
 
7.4%
e 34
 
7.4%
b 34
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 102
22.2%
s 102
22.2%
M 51
11.1%
n 51
11.1%
g 51
11.1%
w 34
 
7.4%
e 34
 
7.4%
b 34
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 102
22.2%
s 102
22.2%
M 51
11.1%
n 51
11.1%
g 51
11.1%
w 34
 
7.4%
e 34
 
7.4%
b 34
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 102
22.2%
s 102
22.2%
M 51
11.1%
n 51
11.1%
g 51
11.1%
w 34
 
7.4%
e 34
 
7.4%
b 34
 
7.4%
Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:57.538742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 186
11.8%
s 179
 
11.3%
e 156
 
9.9%
n 103
 
6.5%
r 102
 
6.5%
t 100
 
6.3%
a 96
 
6.1%
M 79
 
5.0%
g 64
 
4.0%
; 60
 
3.8%
Other values (31) 456
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 186
11.8%
s 179
 
11.3%
e 156
 
9.9%
n 103
 
6.5%
r 102
 
6.5%
t 100
 
6.3%
a 96
 
6.1%
M 79
 
5.0%
g 64
 
4.0%
; 60
 
3.8%
Other values (31) 456
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 186
11.8%
s 179
 
11.3%
e 156
 
9.9%
n 103
 
6.5%
r 102
 
6.5%
t 100
 
6.3%
a 96
 
6.1%
M 79
 
5.0%
g 64
 
4.0%
; 60
 
3.8%
Other values (31) 456
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 186
11.8%
s 179
 
11.3%
e 156
 
9.9%
n 103
 
6.5%
r 102
 
6.5%
t 100
 
6.3%
a 96
 
6.1%
M 79
 
5.0%
g 64
 
4.0%
; 60
 
3.8%
Other values (31) 456
28.8%
Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:06.780180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 186
11.8%
s 179
 
11.3%
e 156
 
9.9%
n 103
 
6.5%
r 102
 
6.5%
t 100
 
6.3%
a 96
 
6.1%
M 79
 
5.0%
g 64
 
4.0%
; 60
 
3.8%
Other values (31) 456
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 186
11.8%
s 179
 
11.3%
e 156
 
9.9%
n 103
 
6.5%
r 102
 
6.5%
t 100
 
6.3%
a 96
 
6.1%
M 79
 
5.0%
g 64
 
4.0%
; 60
 
3.8%
Other values (31) 456
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 186
11.8%
s 179
 
11.3%
e 156
 
9.9%
n 103
 
6.5%
r 102
 
6.5%
t 100
 
6.3%
a 96
 
6.1%
M 79
 
5.0%
g 64
 
4.0%
; 60
 
3.8%
Other values (31) 456
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 186
11.8%
s 179
 
11.3%
e 156
 
9.9%
n 103
 
6.5%
r 102
 
6.5%
t 100
 
6.3%
a 96
 
6.1%
M 79
 
5.0%
g 64
 
4.0%
; 60
 
3.8%
Other values (31) 456
28.8%
Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:06.780180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.364705882
Min length3

Characters and Unicode

Total characters371
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowMissing
3rd rowMissing
4th rowyes
5th rowyes
ValueCountFrequency (%)
yes 56
65.9%
missing 29
34.1%
2025-05-18T21:34:57.948951image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.380952381
Min length3

Characters and Unicode

Total characters452
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowMissing
5th rowMissing
ValueCountFrequency (%)
missing 50
59.5%
web 34
40.5%
2025-05-15T17:32:07.206297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.380952381
Min length3

Characters and Unicode

Total characters452
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowMissing
5th rowMissing
ValueCountFrequency (%)
missing 50
59.5%
web 34
40.5%
2025-05-15T17:32:07.206297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 114
30.7%
i 58
15.6%
y 56
15.1%
e 56
15.1%
M 29
 
7.8%
n 29
 
7.8%
g 29
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 371
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 114
30.7%
i 58
15.6%
y 56
15.1%
e 56
15.1%
M 29
 
7.8%
n 29
 
7.8%
g 29
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 371
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 114
30.7%
i 58
15.6%
y 56
15.1%
e 56
15.1%
M 29
 
7.8%
n 29
 
7.8%
g 29
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 371
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 114
30.7%
i 58
15.6%
y 56
15.1%
e 56
15.1%
M 29
 
7.8%
n 29
 
7.8%
g 29
 
7.8%

tech_tf_tools_used
Real number (ℝ)

Missing  Zeros 

Distinct6
Distinct (%)11.1%
Missing31
Missing (%)36.5%
Infinite0
Infinite (%)0.0%
Mean1.722222222
Minimum0
Maximum7
Zeros21
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:58.150818image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 100
22.1%
s 100
22.1%
M 50
11.1%
n 50
11.1%
g 50
11.1%
W 34
 
7.5%
e 34
 
7.5%
b 34
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 100
22.1%
s 100
22.1%
M 50
11.1%
n 50
11.1%
g 50
11.1%
W 34
 
7.5%
e 34
 
7.5%
b 34
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 100
22.1%
s 100
22.1%
M 50
11.1%
n 50
11.1%
g 50
11.1%
W 34
 
7.5%
e 34
 
7.5%
b 34
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 100
22.1%
s 100
22.1%
M 50
11.1%
n 50
11.1%
g 50
11.1%
W 34
 
7.5%
e 34
 
7.5%
b 34
 
7.5%
Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:07.363348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.285714286
Min length3

Characters and Unicode

Total characters360
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowMissing
3rd rowMissing
4th rowYes
5th rowYes
ValueCountFrequency (%)
yes 57
67.9%
missing 27
32.1%
2025-05-15T17:32:07.935193image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 111
30.8%
Y 57
15.8%
e 57
15.8%
i 54
15.0%
M 27
 
7.5%
n 27
 
7.5%
g 27
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 111
30.8%
Y 57
15.8%
e 57
15.8%
i 54
15.0%
M 27
 
7.5%
n 27
 
7.5%
g 27
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 111
30.8%
Y 57
15.8%
e 57
15.8%
i 54
15.0%
M 27
 
7.5%
n 27
 
7.5%
g 27
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 111
30.8%
Y 57
15.8%
e 57
15.8%
i 54
15.0%
M 27
 
7.5%
n 27
 
7.5%
g 27
 
7.5%

Tech (TF) - Tools Used
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.988095238
Minimum0
Maximum9
Zeros28
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:08.118399image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q32
95-th percentile4.35
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.698130678
Coefficient of variation (CV)0.9860113613
Kurtosis0.342813046
Mean1.722222222
Median Absolute Deviation (MAD)2
Skewness0.7903310464
Sum93
Variance2.883647799
MonotonicityNot monotonic
2025-05-18T21:34:58.333539image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.916912978
Coefficient of variation (CV)0.9641957493
Kurtosis1.467476251
Mean1.988095238
Median Absolute Deviation (MAD)2
Skewness1.068645087
Sum167
Variance3.674555364
MonotonicityNot monotonic
2025-05-15T17:32:08.346453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 21
24.7%
0 21
24.7%
4 7
 
8.2%
3 2
 
2.4%
5 2
 
2.4%
7 1
 
1.2%
(Missing) 31
36.5%
ValueCountFrequency (%)
0 21
24.7%
2 21
24.7%
3 2
 
2.4%
4 7
 
8.2%
5 2
 
2.4%
ValueCountFrequency (%)
7 1
 
1.2%
5 2
 
2.4%
4 7
 
8.2%
3 2
 
2.4%
2 21
24.7%

people_prf_project_manage_changes
Real number (ℝ)

Missing  Zeros 

Distinct3
Distinct (%)10.0%
Missing55
Missing (%)64.7%
Infinite0
Infinite (%)0.0%
Mean0.2
Minimum0
Maximum2
Zeros25
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:58.466380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 32
38.1%
0 28
33.3%
4 14
16.7%
3 2
 
2.4%
7 2
 
2.4%
5 2
 
2.4%
1 2
 
2.4%
6 1
 
1.2%
9 1
 
1.2%
ValueCountFrequency (%)
0 28
33.3%
1 2
 
2.4%
2 32
38.1%
3 2
 
2.4%
4 14
16.7%
ValueCountFrequency (%)
9 1
 
1.2%
7 2
 
2.4%
6 1
 
1.2%
5 2
 
2.4%
4 14
16.7%

People (PRF) - Project manage changes
Real number (ℝ)

Missing  Zeros 

Distinct3
Distinct (%)9.4%
Missing52
Missing (%)61.9%
Infinite0
Infinite (%)0.0%
Mean0.1875
Minimum0
Maximum2
Zeros27
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:08.535095image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 32
38.1%
0 28
33.3%
4 14
16.7%
3 2
 
2.4%
7 2
 
2.4%
5 2
 
2.4%
1 2
 
2.4%
6 1
 
1.2%
9 1
 
1.2%
ValueCountFrequency (%)
0 28
33.3%
1 2
 
2.4%
2 32
38.1%
3 2
 
2.4%
4 14
16.7%
ValueCountFrequency (%)
9 1
 
1.2%
7 2
 
2.4%
6 1
 
1.2%
5 2
 
2.4%
4 14
16.7%

People (PRF) - Project manage changes
Real number (ℝ)

Missing  Zeros 

Distinct3
Distinct (%)9.4%
Missing52
Missing (%)61.9%
Infinite0
Infinite (%)0.0%
Mean0.1875
Minimum0
Maximum2
Zeros27
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:08.535095image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4842341981
Coefficient of variation (CV)2.421170991
Kurtosis6.05659911
Mean0.2
Median Absolute Deviation (MAD)0
Skewness2.498964423
Sum6
Variance0.2344827586
MonotonicityNot monotonic
2025-05-18T21:34:58.724303image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4709290749
Coefficient of variation (CV)2.511621733
Kurtosis6.692112853
Mean0.1875
Median Absolute Deviation (MAD)0
Skewness2.609927853
Sum6
Variance0.2217741935
MonotonicityNot monotonic
2025-05-15T17:32:08.780994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4709290749
Coefficient of variation (CV)2.511621733
Kurtosis6.692112853
Mean0.1875
Median Absolute Deviation (MAD)0
Skewness2.609927853
Sum6
Variance0.2217741935
MonotonicityNot monotonic
2025-05-15T17:32:08.780994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 25
29.4%
1 4
 
4.7%
2 1
 
1.2%
(Missing) 55
64.7%
ValueCountFrequency (%)
0 25
29.4%
1 4
 
4.7%
2 1
 
1.2%
ValueCountFrequency (%)
2 1
 
1.2%
1 4
 
4.7%
0 25
29.4%

people_prf_personnel_changes
Real number (ℝ)

Missing  Zeros 

Distinct5
Distinct (%)16.7%
Missing55
Missing (%)64.7%
Infinite0
Infinite (%)0.0%
Mean0.7666666667
Minimum0
Maximum12
Zeros21
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:58.911403image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 27
32.1%
1 4
 
4.8%
2 1
 
1.2%
(Missing) 52
61.9%
ValueCountFrequency (%)
0 27
32.1%
1 4
 
4.8%
2 1
 
1.2%
ValueCountFrequency (%)
2 1
 
1.2%
1 4
 
4.8%
0 27
32.1%

People (PRF) - Personnel changes
Real number (ℝ)

Missing  Zeros 

Distinct5
Distinct (%)15.6%
Missing52
Missing (%)61.9%
Infinite0
Infinite (%)0.0%
Mean0.78125
Minimum0
Maximum12
Zeros22
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:08.957675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 27
32.1%
1 4
 
4.8%
2 1
 
1.2%
(Missing) 52
61.9%
ValueCountFrequency (%)
0 27
32.1%
1 4
 
4.8%
2 1
 
1.2%
ValueCountFrequency (%)
2 1
 
1.2%
1 4
 
4.8%
0 27
32.1%

People (PRF) - Personnel changes
Real number (ℝ)

Missing  Zeros 

Distinct5
Distinct (%)15.6%
Missing52
Missing (%)61.9%
Infinite0
Infinite (%)0.0%
Mean0.78125
Minimum0
Maximum12
Zeros22
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:08.957675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2.55
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.238893405
Coefficient of variation (CV)2.920295745
Kurtosis23.60303678
Mean0.7666666667
Median Absolute Deviation (MAD)0
Skewness4.678441019
Sum23
Variance5.012643678
MonotonicityNot monotonic
2025-05-18T21:34:59.130056image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2.45
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.181067732
Coefficient of variation (CV)2.791766697
Kurtosis24.15841254
Mean0.78125
Median Absolute Deviation (MAD)0
Skewness4.696312295
Sum25
Variance4.757056452
MonotonicityNot monotonic
2025-05-15T17:32:09.195865image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2.45
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.181067732
Coefficient of variation (CV)2.791766697
Kurtosis24.15841254
Mean0.78125
Median Absolute Deviation (MAD)0
Skewness4.696312295
Sum25
Variance4.757056452
MonotonicityNot monotonic
2025-05-15T17:32:09.195865image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 21
 
24.7%
1 6
 
7.1%
3 1
 
1.2%
2 1
 
1.2%
12 1
 
1.2%
(Missing) 55
64.7%
ValueCountFrequency (%)
0 21
24.7%
1 6
 
7.1%
2 1
 
1.2%
3 1
 
1.2%
12 1
 
1.2%
ValueCountFrequency (%)
12 1
 
1.2%
3 1
 
1.2%
2 1
 
1.2%
1 6
 
7.1%
0 21
24.7%

project_prf_total_project_cost
Real number (ℝ)

Missing 

Distinct27
Distinct (%)96.4%
Missing57
Missing (%)67.1%
Infinite0
Infinite (%)0.0%
Mean100125.6071
Minimum4871
Maximum765000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2025-05-18T21:34:59.473302image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 22
26.2%
1 6
 
7.1%
2 2
 
2.4%
3 1
 
1.2%
12 1
 
1.2%
(Missing) 52
61.9%
ValueCountFrequency (%)
0 22
26.2%
1 6
 
7.1%
2 2
 
2.4%
3 1
 
1.2%
12 1
 
1.2%
ValueCountFrequency (%)
12 1
 
1.2%
3 1
 
1.2%
2 2
 
2.4%
1 6
 
7.1%
0 22
26.2%

Project (PRF) - Total project cost
Real number (ℝ)

Missing 

Distinct28
Distinct (%)96.6%
Missing55
Missing (%)65.5%
Infinite0
Infinite (%)0.0%
Mean106224.7241
Minimum4871
Maximum765000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:09.461774image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 22
26.2%
1 6
 
7.1%
2 2
 
2.4%
3 1
 
1.2%
12 1
 
1.2%
(Missing) 52
61.9%
ValueCountFrequency (%)
0 22
26.2%
1 6
 
7.1%
2 2
 
2.4%
3 1
 
1.2%
12 1
 
1.2%
ValueCountFrequency (%)
12 1
 
1.2%
3 1
 
1.2%
2 2
 
2.4%
1 6
 
7.1%
0 22
26.2%

Project (PRF) - Total project cost
Real number (ℝ)

Missing 

Distinct28
Distinct (%)96.6%
Missing55
Missing (%)65.5%
Infinite0
Infinite (%)0.0%
Mean106224.7241
Minimum4871
Maximum765000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size804.0 B
2025-05-15T17:32:09.461774image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4871
5-th percentile8074.85
Q115580.25
median56237.5
Q373325
95-th percentile539460
Maximum765000
Range760129
Interquartile range (IQR)57744.75

Descriptive statistics

Standard deviation190572.8228
Coefficient of variation (CV)1.9033375
Kurtosis10.36681363
Mean100125.6071
Median Absolute Deviation (MAD)36570
Skewness3.3442196
Sum2803517
Variance3.63180008 × 1010
MonotonicityNot monotonic
2025-05-18T21:34:59.726227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4871
5-th percentile8136.4
Q115680
median57035
Q381500
95-th percentile569800
Maximum765000
Range760129
Interquartile range (IQR)65820

Descriptive statistics

Standard deviation189999.2288
Coefficient of variation (CV)1.78865354
Kurtosis9.500334319
Mean106224.7241
Median Absolute Deviation (MAD)39875
Skewness3.172943192
Sum3080517
Variance3.609970696 × 1010
MonotonicityNot monotonic
2025-05-15T17:32:09.700533image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
765000 2
 
2.4%
69850 1
 
1.2%
61435 1
 
1.2%
15680 1
 
1.2%
68200 1
 
1.2%
7644 1
 
1.2%
31465 1
 
1.2%
120600 1
 
1.2%
70600 1
 
1.2%
10180 1
 
1.2%
Other values (17) 17
 
20.0%
(Missing) 57
67.1%
ValueCountFrequency (%)
4871 1
1.2%
7644 1
1.2%
8875 1
1.2%
10180 1
1.2%
13880 1
1.2%
ValueCountFrequency (%)
765000 2
2.4%
120600 1
1.2%
104200 1
1.2%
100200 1
1.2%
90300 1
1.2%
Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-05-18T21:34:59.891291image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
765000 2
 
2.4%
90300 1
 
1.2%
15680 1
 
1.2%
68200 1
 
1.2%
7644 1
 
1.2%
31465 1
 
1.2%
120600 1
 
1.2%
70600 1
 
1.2%
10180 1
 
1.2%
30300 1
 
1.2%
Other values (18) 18
 
21.4%
(Missing) 55
65.5%
ValueCountFrequency (%)
4871 1
1.2%
7644 1
1.2%
8875 1
1.2%
10180 1
1.2%
13880 1
1.2%
ValueCountFrequency (%)
765000 2
2.4%
277000 1
1.2%
120600 1
1.2%
104200 1
1.2%
100200 1
1.2%
Distinct4
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:09.939059image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
765000 2
 
2.4%
90300 1
 
1.2%
15680 1
 
1.2%
68200 1
 
1.2%
7644 1
 
1.2%
31465 1
 
1.2%
120600 1
 
1.2%
70600 1
 
1.2%
10180 1
 
1.2%
30300 1
 
1.2%
Other values (18) 18
 
21.4%
(Missing) 55
65.5%
ValueCountFrequency (%)
4871 1
1.2%
7644 1
1.2%
8875 1
1.2%
10180 1
1.2%
13880 1
1.2%
ValueCountFrequency (%)
765000 2
2.4%
277000 1
1.2%
120600 1
1.2%
104200 1
1.2%
100200 1
1.2%
Distinct4
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size804.0 B
2025-05-15T17:32:09.939059image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length14
Median length7
Mean length9.388235294
Min length7

Characters and Unicode

Total characters798
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing
2nd roweuropean, euro
3rd roweuropean, euro
4th rowMissing
5th rowMissing
ValueCountFrequency (%)
missing 56
49.1%
european 16
 
14.0%
euro 16
 
14.0%
canada 13
 
11.4%
dollar 13
 
11.4%
2025-05-18T21:35:00.284454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length21
Median length7
Mean length9.583333333
Min length7

Characters and Unicode

Total characters805
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowMissing
2nd rowEuropean, euro
3rd rowEuropean, euro
4th rowMissing
5th rowMissing
ValueCountFrequency (%)
missing 54
47.0%
european 16
 
13.9%
euro 16
 
13.9%
dollar 14
 
12.2%
canada 13
 
11.3%
united 1
 
0.9%
states 1
 
0.9%
2025-05-15T17:32:10.362391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length21
Median length7
Mean length9.583333333
Min length7

Characters and Unicode

Total characters805
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowMissing
2nd rowEuropean, euro
3rd rowEuropean, euro
4th rowMissing
5th rowMissing
ValueCountFrequency (%)
missing 54
47.0%
european 16
 
13.9%
euro 16
 
13.9%
dollar 14
 
12.2%
canada 13
 
11.3%
united 1
 
0.9%
states 1
 
0.9%
2025-05-15T17:32:10.362391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 112
14.0%
s 112
14.0%
n 85
10.7%
a 68
8.5%
M 56
7.0%
g 56
7.0%
e 48
 
6.0%
r 45
 
5.6%
o 45
 
5.6%
u 32
 
4.0%
Other values (6) 139
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 112
14.0%
s 112
14.0%
n 85
10.7%
a 68
8.5%
M 56
7.0%
g 56
7.0%
e 48
 
6.0%
r 45
 
5.6%
o 45
 
5.6%
u 32
 
4.0%
Other values (6) 139
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 112
14.0%
s 112
14.0%
n 85
10.7%
a 68
8.5%
M 56
7.0%
g 56
7.0%
e 48
 
6.0%
r 45
 
5.6%
o 45
 
5.6%
u 32
 
4.0%
Other values (6) 139
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 112
14.0%
s 112
14.0%
n 85
10.7%
a 68
8.5%
M 56
7.0%
g 56
7.0%
e 48
 
6.0%
r 45
 
5.6%
o 45
 
5.6%
u 32
 
4.0%
Other values (6) 139
17.4%

Report generated by YData.